Evolutionary Multi-Objective Optimization for Virtual Network Function Placement
Joseph Billingsley+, Ke Li, Geyong Min, Nektarios Georgalas
CoRR abs/2106.14727 | March 2023 PDF |
Supp | BiB
@article{BillingsleyLMMG22,
author = {Joseph Billingsley and
Ke Li and
Wang Miao and
Geyong Min and
Nektarios Georgalas},
title = {Evolutionary Multi-Objective Virtual Network Function Placement: {A}
Formal Model and Effective Algorithms},
journal = {CoRR},
volume = {abs/2106.14727},
year = {2021},
url = {https://arxiv.org/abs/2106.14727},
eprinttype = {arXiv},
eprint = {2106.14727},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-14727.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks
Yingying Guan+, Qingyang Song, Weijing Qi, Ke Li, Lei Guo, Abbas Jamalipour
CoRR abs/2303.15878 | February 2023 PDF | BiB
@article{GuanSQLGJ23,
author = {Yingying Guan and
Qingyang Song and
Weijing Qi and
Ke Li and
Lei Guo and
Abbas Jamalipour},
title = {Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks},
journal = {CoRR},
volume = {abs/2301.12148},
year = {2023},
url = {https://arxiv.org/abs/2301.12148},
eprinttype = {arXiv},
eprint = {2301.12148}
}
Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis
Ryoji Tanabe, Ke Li
CoRR abs/2301.12148 | January 2023 PDF | Supp | BiB
@article{TanabeL23,
author = {Ryoji Tanabe and
Ke Li},
title = {Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis},
journal = {CoRR},
volume = {abs/2301.12148},
year = {2023},
url = {https://arxiv.org/abs/2301.12148},
eprinttype = {arXiv},
eprint = {2301.12148}
}
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments Ke Li*, Renzhi Chen+*, Xin Yao
CoRR abs/2211.02879 | November 2022 PDF |
Supp | BiB
@article{LiCY22,
author = {Ke Li and
Renzhi Chen and
Xin Yao},
title = {A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments},
journal = {CoRR},
volume = {abs/2211.0287},
year = {2022},
url = {https://arxiv.org/abs/2211.02879},
eprinttype = {arXiv},
eprint = {2211.02879}
}
DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls Ke Li, Heng Yang+, Willem Visser
CoRR abs/2206.05743 | June 2022 PDF | BiB
@article{LiYV22,
author = {Ke Li and
Heng Yang and
Willem Visser},
title = {DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls},
journal = {CoRR},
volume = {abs/2206.05743},
year = {2022},
url = {https://arxiv.org/abs/2206.05743},
doi = {10.48550/arXiv.2206.05743},
eprinttype = {arXiv},
eprint = {2206.05743}
}
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art
Phoenix Williams+, Ke Li
CoRR abs/2203.04405 | March 2022 PDF | BiB
@article{WilliamsL22,
author = {Phoenix Neale Williams and
Ke Li},
title = {Art-Attack: Black-Box Adversarial Attack via Evolutionary Art},
journal = {CoRR},
volume = {abs/2203.04405},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.04405},
doi = {10.48550/arXiv.2203.04405},
eprinttype = {arXiv},
eprint = {2203.04405},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-04405.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Automated Few-Shot Time Series Forecasting based on Bi-level Programming
Jiangjiao Xu+, Ke Li
CoRR abs/2203.03328 | March 2022 PDF | BiB
@article{XuL22,
author = {Jiangjiao Xu and
Ke Li},
title = {Automated Few-Shot Time Series Forecasting based on Bi-level Programming},
journal = {CoRR},
volume = {abs/2203.03328},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.03328},
doi = {10.48550/arXiv.2203.03328},
eprinttype = {arXiv},
eprint = {2203.03328},
timestamp = {Wed, 16 Mar 2022 16:39:52 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-03328.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
LONViZ: Unboxing the Black-Box of Configurable Software Systems from a Complex Networks Perspective Ke Li, Peili Mao+, Tao Chen
CoRR abs/2201.01429 | January 2022 PDF | BiB
@article{LiMC22,
author = {Ke Li and
Peili Mao and
Tao Chen},
title = {LONViZ: Unboxing the black-box of Configurable Software Systems from
a Complex Networks Perspective},
journal = {CoRR},
volume = {abs/2201.01429},
year = {2022},
url = {https://arxiv.org/abs/2201.01429},
eprinttype = {arXiv},
eprint = {2201.01429},
timestamp = {Mon, 10 Jan 2022 13:39:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-01429.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities Ke Li
CoRR abs/2108.09588 | August 2021 PDF | BiB
Multioutput Surrogate Assisted Evolutionary Algorithm for Expensive Multi-Modal Optimization Problems
Renzhi Chen+, Ke Li
Proc. of the 2023 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'23) Abs | PDF | BiB
Empirical Studies of Resampling Strategies in Noisy Evolutionary Multi-Objective Optimization
Shasha Zhou+, Ke Li
Proc. of the 2023 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'23) Abs | PDF | BiB
Preference-Based Multi-Objective Optimization with Gaussian Process
Tian Huang+, Ke Li
Proc. of the 2023 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'23) Abs | PDF | BiB
Boosting Text Augmentation via Hybrid Instance Filtering Framework
Heng Yang+, Ke Li
Porc. of the 61st Annual Meeting of the Association for Computational Linguistics
(ACL'23) Abs | PDF | BiB
Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems
Mingyu Huang+, Ke Li
Porc. of the 32nd International Joint Conference on Artificial Intelligence
(IJCAI'23) Abs | PDF |
Supp | BiB | ≈15%
One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem instance, we expect it will also be effective for other instances whose fitness landscapes essentially share structural similarities with each other. However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios. To bridge this gap, by using local optima network as a proxy of fitness landscapes, this paper proposed to leverage graph data mining techniques to conduct qualitative and quantitative analyses to explore the latent topological structural information embedded in those landscapes. In our experiments, we use the number partitioning problem as the case and our empirical results are inspiring to support the overall assumption of the existence of structural similarity between landscapes within neighboring dimensions. Besides, experiments on simulated annealing demonstrate that the performance of a meta-heuristic solver is similar on structurally similar landscapes.
A Surrogate Assisted Evolutionary Strategy for Image Approximation by Density-Ratio Estimation
Phoenix Williams+, Ke Li
Proc. of 2023 IEEE Congress on Evolutionary Computation
(CEC'23) Abs | PDF | BiB
A Multi-Population Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multi-Objective Problems with Multi-Constraint
Juan Zou, Ruiqing Sun, Yuan Liu, Yaru Hu, Shengxiang Yang, Jinhua Zheng, Ke Li
IEEE Trans. Evolutionary Computation (TEVC) 10.1109/TEVC.2023.3260306 Abs | PDF | BiB
In science and engineering, multi-objective optimization problems usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This paper aims to solve the challenges brought by multiple complex constraints. First, this paper analyzes the relationship between single constrained Pareto Front (SCPF) and their common Pareto Front sub-constrained Pareto Front (SubCPF). Next, we discussed the SCPF, SubCPF, and Unconstrainti Pareto Front (UPF)’s help to solve constraining Pareto Front (CPF). Then further discusses what kind of cooperation should be used between multiple populations constrained multi-objective optimization algorithm (CMOEA) to better deal with multi-constrained multi-objective optimization problems (mCMOPs). At the same time, based on the discussion in this paper, we propose a new multi-population CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed Activation Dormancy Detection (ADD) to accelerate the optimization process and uses the proposed Combine Occasion Detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation
Phoenix Williams+, Ke Li
Proc. of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR'23) Abs | PDF | BiB | ≈25%
Deep neural networks (DNNs) are susceptible to adversarial images, raising concerns about their reliability in safety-critical tasks. Sparse adversarial attacks, which limit the number of modified pixels, have shown to be highly effective in causing DNNs to misclassify. However, existing methods often struggle to simultaneously minimize the number of modified pixels and the size of the modifications, often requiring a large number of queries and assuming unrestricted access to the targeted DNN. In contrast, other methods that limit the number of modified pixels often permit unbounded modifications, making them easily detectable. To address these limitations, we propose a novel multi-objective sparse attack algorithm that efficiently minimizes the number of modified pixels and their size during the attack process. Our algorithm draws inspiration from evolutionary computation and incorporates a mechanism for prioritizing objectives that aligns with an attacker's goals. Our approach outperforms existing sparse attacks on CIFAR-10 and ImageNet trained DNN classifiers while requiring only a small query budget, attaining competitive attack success rates while perturbing fewer pixels. Overall, our proposed attack algorithm provides a solution to the limitations of current sparse attack methods by jointly minimizing the number of modified pixels and their size. Our results demonstrate the effectiveness of our approach in restricted scenarios, highlighting its potential to enhance DNN security.
Evolutionary Bi-level Optimization via Multi-objective Transformation Based Lower Level Search
Lei Chen, Hai-Lin Liu, Ke Li, Kay Chen Tan
IEEE Trans. Evolutionary Computation (TEVC) 10.1109/TEVC.2023.3236455 Abs | PDF | BiB
Nested evolutionary algorithms (EAs) have been regarded as very promising tools for bi-level optimization. Due to the nested structure, the upper level population evaluation requires a set of complete lower level optimizations, thereby reducing the efficiency and practicability of EA methods. In this paper, a multi-objective transformation-based evolutionary algorithm (MOTEA) is proposed to perform multiple lower level optimizations in a parallel and collaborative manner. Specifically, the corresponding multiple lower level optimizations for each generation of the upper level population evaluation are transformed into locating a set of Pareto optimal solutions of a constructed multi-objective optimization problem. By utilizing the built-in implicit parallelism of evolutionary multi-objective optimization, multiple lower level problems can thus be optimized in parallel. Within one multi-objective search population, the collaboration among the parallel lower level optimization can be realized by exploiting and utilizing the implicit similarities among them for better efficiency. The effectiveness and efficiency of the proposed MOTEA are verified by comparing it with four state-of-the-art evolutionary bi-level optimization algorithms on two sets of popular bi-level optimization benchmark test problems and three application problems.
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank Ke Li, Guiyu Lai+, Xin Yao
IEEE Trans. Evolutionary Computation (TEVC) 10.1109/TEVC.2023.3234269 Abs |
PDF |
Supp | BiB
In practical multi-criterion decision-making, it is cumbersome if a decision maker (DM) is asked to choose among a set of trade-off alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multi-objective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multi-objective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicting criteria. Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm. Note that this framework is so general that any existing EMO algorithm can be applied in a plug-in manner. Experiments on 48 benchmark test problems with up to 10 objectives and a real-world multi-objective robot control problem fully demonstrate the effectiveness of our proposed algorithms for finding SOI.
@article{LiLY22,
author = {Ke Li and
Guiyu Lai and
Xin Yao},
title = {Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank},
journal = {CoRR},
volume = {abs/2204.02604},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2204.02604},
doi = {10.48550/arXiv.2204.02604},
eprinttype = {arXiv},
eprint = {2204.02604},
timestamp = {Tue, 12 Apr 2022 18:42:14 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2204-02604.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars
Bo Lyu+, Maher Hamdi, Yin Yang, Yuting Cao, Zheng Yan, Ke Li, Shiping Wen, Tingwen Huang
IEEE Trans. Emerging Topics in Computational Intelligence (TETCI), 7(2): 415-425, 2023 10.1109/TETCI.2022.3210998 Abs |
PDF | BiB
Multi-objective optimization problems are ubiquitous in real-world science, engineering and design optimization problems. It is not uncommon that the objective functions are as a black box, the evaluation of which usually involve time-consuming and/or costly physical experiments. Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model. In this paper, we propose a framework for implementing batched data-driven evolutionary multi-objective optimization. It is so general that any off-the-shelf evolutionary multi-objective optimization algorithms can be applied in a plug-in manner. There are two unique components: 1) based on the Karush-Kuhn-Tucker conditions, a manifold interpolation approach that explores more diversified solutions with a convergence guarantee along the manifold of the approximated Pareto-optimal set; and 2) a batch recommendation approach that reduces the computational time of the data-driven evolutionary optimization process by evaluating multiple samples at a time in parallel. Comparing against 7 state-of-the-art surrogate-assisted evolutionary algorithms, experiments on 168 benchmark test problem instances with various properties and a real-world application on hyper-parameter optimization fully demonstrate the effectiveness and superiority of our proposed framework, which is featured with a faster convergence and a stronger resilience to various PF shapes.
@article{LyuHYCYLSH22,
author = {Bo Lyu and
Maher Hamdi and
Yin Yang and
Yuting Cao and
Zheng Yan and
Ke Li and
Shiping Wen and
Tingwen Huang},
journal = {IEEE Trans. Emerg. Top. Comput. Intell.},
title = {Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars},
year = {2022},
pages = {1-11},
note = {accepted for publication},
doi = {10.1109/TETCI.2022.3210998}
}
Neural Architecture Search for Portrait Parsing
Bo Lyu+, Yin Yang, Shiping Wen, Tingwen Huang, Ke Li
IEEE Trans. Neural Networks and Learning Systems (TNNLS), 34(3): 1112-1121, 2023 10.1109/TNNLS.2021.3104872 Abs |
PDF | BiB
This work proposes a neural architecture search (NAS) method for portrait parsing, which is a novel up-level task based on portrait segmentation and face labeling. Recently, NAS has become an effective method in terms of automatic machine learning. However, remarkable achievements have been made only in image classification and natural language processing (NLP) areas. Meanwhile, state-of-the-art portrait segmentation and face labeling approaches are all manually designed, but few models reach a tradeoff between efficiency and performance. Thus, we are extremely interested in improving existing NAS methods for dense-per-pixel prediction tasks on portrait datasets. To achieve that, we resort to a cell-based encoder-decoder architecture with an elaborate design of connectivity structure and searching space. As a result, we achieve state-of-the-art performance on three portrait tasks, including 96.8% MIOU on EG1800 (portrait segmentation), 91.2% overall F1-score on HELEN (face labeling), and 95.1% overall F1-score on CelebAMask-HQ (portrait parsing) with only 2.29M model parameters. That is, our approach compares favorably with all previous works on portrait datasets. More crucially, we empirically prove that even a fundamental encoder-decoder architecture may reach an outstanding result on the aforementioned tasks with the help of the innovative approach of NAS. To the best of our knowledge, our work is also the first to report the success of applying NAS on these portrait tasks.
@ARTICLE{LyuYWHL22,
author = {Lyu, Bo and
Yang, Yin and
Wen, Shiping and
Huang, Tingwen and
Li, Ke},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
title = {Neural Architecture Search for Portrait Parsing},
year = {2021},
doi = {10.1109/TNNLS.2021.3104872}
}
Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation Ke Li*, Renzhi Chen+*
IEEE Trans. Evolutionary Computation (TEVC), 27(1): 126-140, 2023 10.1109/TEVC.2022.3162993 Abs |
PDF |
Supp | BiB
Multi-objective optimization problems are ubiquitous in real-world science, engineering and design optimization problems. It is not uncommon that the objective functions are as a black box, the evaluation of which usually involve time-consuming and/or costly physical experiments. Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model. In this paper, we propose a framework for implementing batched data-driven evolutionary multi-objective optimization. It is so general that any off-the-shelf evolutionary multi-objective optimization algorithms can be applied in a plug-in manner. There are two unique components: 1) based on the Karush-Kuhn-Tucker conditions, a manifold interpolation approach that explores more diversified solutions with a convergence guarantee along the manifold of the approximated Pareto-optimal set; and 2) a batch recommendation approach that reduces the computational time of the data-driven evolutionary optimization process by evaluating multiple samples at a time in parallel. Comparing against 7 state-of-the-art surrogate-assisted evolutionary algorithms, experiments on 168 benchmark test problem instances with various properties and a real-world application on hyper-parameter optimization fully demonstrate the effectiveness and superiority of our proposed framework, which is featured with a faster convergence and a stronger resilience to various PF shapes.
@ARTICLE{LiC22,
author = {Li, Ke and
Chen, Renzhi},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation},
year = {2022},
doi = {10.1109/TEVC.2022.3162993}}
MTLP-JR: Multi-Task Learning-Based Prediction for Joint Ranking in Neural Architecture Search
Bo Lyu+, Longfei Lu, Maher Hamdi, Shiping Wen, Yin Yang, Ke Li
Computers & Electrical Engineering, 105: 108474, 2023 10.1016/j.compeleceng.2022.108474 Abs |
PDF | BiB
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
Renzhi Chen+, Ke Li
Proc. of the 12th International Conference on Evolutionary Multi-Criterion Optimization
(EMO'23), Springer, p. 56-70, March, 2023 10.1007/978-3-031-27250-9_5 Abs |
PDF |
Supp | BiB
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current data-driven EMO does not fully leverage the latent information of the surrogate model. Bearing these considerations in mind, this paper proposes a data-driven EMO algorithm based on multiple-gradient descent. By leveraging the regularity information provided by the up-to-date surrogate model, it is able to progressively probe a set of well distributed candidate solutions with a convergence guarantee. In addition, its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations. Experiments on 33 benchmark test problem instances with disconnected PFs fully demonstrate the effectiveness of our proposed method against four selected peer algorithms.
Sparse Adversarial Attack via Bi-Objective Optimization
Phoenix Williams+, Ke Li, Geyong Min
Proc. of the 12th International Conference on Evolutionary Multi-Criterion Optimization
(EMO'23), Springer, p. 118-133, March, 2023 10.1007/978-3-031-27250-9_9 Abs |
PDF | BiB
Posterior Decision-Making Based on Decomposition-Driven Knee Point Identification Ke Li, Haifeng Nie+, Huiru Gao+, Xin Yao
IEEE Trans. Evolutionary Computation (TEVC), 26(6): 1409-1423, 2022 10.1109/TEVC.2021.3116121 Abs |
PDF |
Code |
Supp | BiB
Knee points, characterized as a small improvement on one objective can lead to a significant degradation on at least one of the other objectives, are attractive to decision makers in multi-criterion decision-making. This paper presents a simple and effective knee point identification method to help decision makers identify solution(s) of interest from a given set of trade-off solutions thus facilitating posterior decision-making. Our basic idea is to sequentially validate whether a solution is a knee point or not by comparing its localized trade-off utility with others within its neighborhood characterized from a decomposition perspective. In particular, a solution is a knee point if and only if it has the best localized trade-off utility among its neighbors. We implement a GPU version that carries out the knee point identification in a parallel manner. This GPU version reduces the worst-case complexity from quadratic to linear. The performance of our proposed method is compared with five state-of-the-art knee point identification methods on 134 test problem instances and two real-world engineering design problems. Empirical results demonstrate its outstanding performance especially on problems with many local knee points. We further validate the usefulness of our proposed method for guiding evolutionary multi-objective optimization algorithms to search for knee points on the fly during the evolutionary process.
@article{LiNGY22,
author = {Li, Ke and
Nie, Haifeng and
Gao, Huiru and
Yao, Xin},
journal = {IEEE Transactions on Evolutionary Computation},
title = {Posterior Decision-Making Based on Decomposition-Driven Knee Point Identification},
year = {2021},
publisher = {IEEE}
}
Distributed UAV Swarm Formation and Collision Avoidance Strategies Over Fixed and Switching Topologies
Jia Wu, Chunbo Luo, Yang Luo, Ke Li
IEEE Trans. Cybernetics (TCYB), 52(10): 10969-10979, 2022 10.1109/TCYB.2021.3132587 Abs |
PDF | BiB
This article proposes a controlling framework for multiple unmanned aerial vehicles (UAVs) to integrate the modes of formation flight and swarm deployment over fixed and switching topologies. Formation strategies enable UAVs to enjoy key collective benefits including reduced energy consumption, but the shape of the formation and each UAV's freedom are significantly restrained. Swarm strategies are thus proposed to maximize each UAV's freedom following simple yet powerful rules. This article investigates the integration and switch between these two strategies, considering the deployment environment factors, such as poor network conditions and unknown and often highly mobile obstacles. We design a distributed formation controller to guide multiple UAVs in orderless states to swiftly reach an intended formation. Inspired by starling birds and similar biological creatures, a distributed collision avoidance controller is proposed to avoid unknown and mobile obstacles. We further illustrated the stability of the controllers over both fixed and switching topologies. The experimental results confirm the effectiveness of the framework.
@article{WuLLL22,
author = {Jia Wu and
Chunbo Luo and
Yang Luo and
Ke Li},
title = {Distributed {UAV} Swarm Formation and Collision Avoidance Strategies
Over Fixed and Switching Topologies},
journal = {{IEEE} Trans. Cybern.},
volume = {52},
number = {10},
pages = {10969--10979},
year = {2022},
url = {https://doi.org/10.1109/TCYB.2021.3132587},
doi = {10.1109/TCYB.2021.3132587},
timestamp = {Thu, 13 Oct 2022 16:02:10 +0200},
biburl = {https://dblp.org/rec/journals/tcyb/WuLLL22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Transfer Learning Based Parallel Evolutionary Algorithm Framework for Bi-level Optimization
Lei Chen, Hai-Lin Liu, Kay Chen Tan, Ke Li
IEEE Trans. Evolutionary Computation (TEVC), 26(1): 115-129, 2022 10.1109/TEVC.2021.3095313 Abs |
PDF | Code | BiB
Evolutionary algorithms (EAs) have been recognized as a promising approach for bilevel optimization. However, the population-based characteristic of EAs largely influences their efficiency and effectiveness due to the nested structure of the two levels of optimization problems. In this article, we propose a transfer learning-based parallel EA (TLEA) framework for bilevel optimization. In this framework, the task of optimizing a set of lower level problems parameterized by upper level variables is conducted in a parallel manner. In the meanwhile, a transfer learning strategy is developed to improve the effectiveness of each lower level search (LLS) process. In practice, we implement two versions of the TLEA: the first version uses the covariance matrix adaptation evolutionary strategy and the second version uses the differential evolution as the evolutionary operator in lower level optimization. The experimental studies on two sets of widely used bilevel optimization benchmark problems are conducted, and the performance of the two TLEA implementations is compared to that of four well-established evolutionary bilevel optimization algorithms to verify the effectiveness and efficiency of the proposed algorithm framework.
@article{ChenLTL22,
author = {Lei Chen and
Hai{-}Lin Liu and
Kay Chen Tan and
Ke Li},
title = {Transfer Learning-Based Parallel Evolutionary Algorithm Framework
for Bilevel Optimization},
journal = {{IEEE} Trans. Evol. Comput.},
volume = {26},
number = {1},
pages = {115--129},
year = {2022},
url = {https://doi.org/10.1109/TEVC.2021.3095313},
doi = {10.1109/TEVC.2021.3095313},
timestamp = {Wed, 23 Feb 2022 11:16:38 +0100},
biburl = {https://dblp.org/rec/journals/tec/ChenLTL22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Preference based Multi-Objective Reinforcement Learning for Multi-Microgrid System Optimization Problem in Smart Grid
Jiangjiao Xu+, Ke Li, Mohammad Abusara
Memetic Computing (MC), 14(2): 225-235, 2022 10.1007/s12293-022-00357-w Abs |
PDF | BiB
Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method.
@article{XuLA22,
author = {Jiangjiao Xu and
Ke Li and
Mohammad Abusara},
title = {Preference based multi-objective reinforcement learning for multi-microgrid
system optimization problem in smart grid},
journal = {Memetic Comput.},
volume = {14},
number = {2},
pages = {225--235},
year = {2022},
url = {https://doi.org/10.1007/s12293-022-00357-w},
doi = {10.1007/s12293-022-00357-w},
timestamp = {Thu, 02 Jun 2022 16:43:14 +0200},
biburl = {https://dblp.org/rec/journals/memetic/XuLA22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Surrogate-Assisted Evolutionary Multi-Objective Optimization for Hardware Design Space Exploration
Renzhi Chen+, Ke Li
Proc. of the NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022 Abs |
PDF | BiB
Hardware design space exploration (DSE) aims to find a suitable micro-architecture for the dedicated hardware accelerators. It is a computationally expensive black-box optimization problem with more than one conflicting performance indicator. Surrogate-assisted evolutionary algorithm is a promising framework for expensive multi-objective optimization problems given its surrogate modeling for handling expensive objective functions and population-based characteristics that search for a set of trade-off solutions simultaneously. However, most, if not all, existing studies mainly focus ‘regular’ Pareto-optimal fronts (PFs), whereas the PF is typically irregular in hardware DSE. In the meanwhile, the gradient information of the differentiable surrogate model(s) is beneficial to navigate a more effective exploration of the search space, but it is yet fully exploited. This paper proposes a surrogate-assisted evolutionary multi-objective optimization based on multiple-gradient descent (MGD) for hardware DSE. Empirical results on both synthetic problems with irregular PFs and real-world hardware DSE cases fully demonstrate the effectiveness and outstanding performance of our proposed algorithm.
Are All Training Data Useful? A Empirical Revisit of Subset Selection in Bayesian Optimization
Peili Mao+, Ke Li
Proc. of the NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022 Abs |
PDF | BiB
Bayesian optimization (BO) has been widely recognized as a powerful approach for black-box optimization problems with expensive objective function(s). Gaussian process (GP), which has been widely used for surrogate modeling in BO, is notorious for its cubic computational complexity grows with the increase of the amount of evaluated samples. This can lead to a significantly increased computational time for BO due to its sequential decision-making nature. This paper revisit the simple and effective subset selection methods to pick up a small group of representative data from the entire dataset to carry out the training and inference of GP in the context of BO. Empirical studies demonstrate that subset selection methods not only promote the performance of the vanilla BO but also significantly reduce the computational time for up to 98%.
Imputation and Forecasting for Multi-Output Gaussian Process in Smart Grid
Jiangjiao Xu+, Ke Li
Proc. of the NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022 Abs |
PDF | BiB
Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?
Shuang Li+, Ke Li, Wei Li
Proc. of the 17the International Conference on Parallel Problem Solving from Nature
(PPSN'22), Springer, p. 124-137, September, 2022 10.1007/978-3-031-14721-0_9 PDF | BiB
@inproceedings{LiLL22,
author = {Shuang Li and
Ke Li and
Wei Li},
title = {Do We Really Need to Use Constraint Violation in Constrained Evolutionary
Multi-objective Optimization?},
booktitle = {PPSN'22: Proc. of 17th International Conference on Parallel Problem Solving from Nature},
series = {Lecture Notes in Computer Science},
volume = {13399},
pages = {124--137},
publisher = {Springer},
year = {2022},
url = {https://doi.org/10.1007/978-3-031-14721-0\_9},
doi = {10.1007/978-3-031-14721-0\_9},
timestamp = {Thu, 25 Aug 2022 08:35:33 +0200},
biburl = {https://dblp.org/rec/conf/ppsn/LiLL22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing
Shasha Zhou+, Ke Li, Geyong Min
Proc. of the 17the International Conference on Parallel Problem Solving from Nature
(PPSN'22), Springer, p. 341-355, September, 2022 10.1007/978-3-031-14714-2_24 PDF | BiB
@inproceedings{ZhouLM22_b,
author = {Shasha Zhou and
Ke Li and
Geyong Min},
title = {Attention-Based Genetic Algorithm for Adversarial Attack in Natural
Language Processing},
booktitle = {PPSN'22: Proc. of 17th International Conference on Parallel Problem Solving from Nature},
series = {Lecture Notes in Computer Science},
volume = {13398},
pages = {341--355},
publisher = {Springer},
year = {2022},
url = {https://doi.org/10.1007/978-3-031-14714-2\_24},
doi = {10.1007/978-3-031-14714-2\_24},
timestamp = {Thu, 25 Aug 2022 08:35:33 +0200},
biburl = {https://dblp.org/rec/conf/ppsn/ZhouLM22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Black-Box Adversarial Attack via Overlapped Shapes
Phoenix Williams+, Ke Li, Geyong Min
Proc. of the 24th Annual Conference on Genetic and Evolutionary Computation
(GECCO’22), ACM, p. 467-468, July, 2022 10.1145/3520304.3528934 PDF | BiB
@inproceedings{Williams0M22,
author = {Phoenix Neale Williams and
Ke Li and
Geyong Min},
title = {Black-box adversarial attack via overlapped shapes},
booktitle = {{GECCO}'22: Proc. of Genetic and Evolutionary Computation Conference, Companion
Volume},
pages = {467--468},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3520304.3528934},
doi = {10.1145/3520304.3528934},
timestamp = {Mon, 25 Jul 2022 17:04:27 +0200},
biburl = {https://dblp.org/rec/conf/gecco/Williams0M22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Adversarial Attack Example Generation via Genetic Algorithm: A Preliminary Result
Shasha Zhou+, Ke Li, Geyong Min
Proc. of the 24th Annual Conference on Genetic and Evolutionary Computation
(GECCO’22), ACM, p. 469-470, July, 2022 10.1145/3520304.3528981 PDF | BiB
@inproceedings{ZhouLM22,
author = {Shasha Zhou and
Ke Li and
Geyong Min},
title = {Adversarial example generation via genetic algorithm: a preliminary
result},
booktitle = {{GECCO}'22: Proc. of Genetic and Evolutionary Computation Conference, Companion
Volume},
pages = {469--470},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3520304.3528981},
doi = {10.1145/3520304.3528981},
timestamp = {Mon, 25 Jul 2022 17:04:27 +0200},
biburl = {https://dblp.org/rec/conf/gecco/Zhou0M22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Decomposition Multi-Objective Optimisation: Current Developments and Future Opportunities Ke Li, Qingfu Zhang
Proc. of the 24th Annual Conference on Genetic and Evolutionary Computation
(GECCO’22), ACM, p. 469-470, July, 2022 10.1145/3520304.3533629 PDF | BiB
@inproceedings{LiZ22,
author = {Ke Li and
Qingfu Zhang},
title = {Decomposition multi-objective optimisation: current developments and
future opportunities},
booktitle = {{GECCO}'22: Proc. of the Genetic and Evolutionary Computation Conference, Companion
Volume},
pages = {1554--1571},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3520304.3533629},
doi = {10.1145/3520304.3533629},
timestamp = {Mon, 25 Jul 2022 17:04:27 +0200},
biburl = {https://dblp.org/rec/conf/gecco/LiZ22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Vertical Distance-based Clonal Selection Mechanism for the Multiobjective Immune Algorithm
Lingjie Li+, Qiuzhen Lin, Ke Li, Zhong Ming
Swarm and Evolutionary Computation (SWEVO), 106: 107299, 2021 10.1016/j.swevo.2021.100886 PDF | BiB
@article{LiLLM21,
author = {Lingjie Li and
Qiuzhen Lin and
Ke Li and
Zhong Ming},
title = {Vertical distance-based clonal selection mechanism for the multiobjective
immune algorithm},
journal = {Swarm Evol. Comput.},
volume = {63},
pages = {100886},
year = {2021},
url = {https://doi.org/10.1016/j.swevo.2021.100886},
doi = {10.1016/j.swevo.2021.100886},
timestamp = {Tue, 15 Jun 2021 09:16:57 +0200},
biburl = {https://dblp.org/rec/journals/swevo/LiLLM21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Vector Angles-Based Many-Objective Particle Swarm Optimization Algorithm Using Archive
Lei Yang+, Xin Hu, Ke Li
Applied Soft Computing (ASOC), 63: 100886, 2021 10.1016/j.asoc.2021.107299 PDF | BiB
@article{YangHL21,
author = {Lei Yang and
Xin Hu and
Ke Li},
title = {A vector angles-based many-objective particle swarm optimization algorithm
using archive},
journal = {Appl. Soft Comput.},
volume = {106},
pages = {107299},
year = {2021},
url = {https://doi.org/10.1016/j.asoc.2021.107299},
doi = {10.1016/j.asoc.2021.107299},
timestamp = {Fri, 03 Dec 2021 13:16:58 +0100},
biburl = {https://dblp.org/rec/journals/asc/YangHL21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
Ran Wang, Suhe Ye, Ke Li, Sam Kwong
Information Sciences (INS), 554: 256–275, 2021 10.1016/j.ins.2020.12.010 PDF | BiB
@article{WangYLK21,
author = {Ran Wang and
Suhe Ye and
Ke Li and
Sam Kwong},
title = {Bayesian network based label correlation analysis for multi-label
classifier chain},
journal = {Inf. Sci.},
volume = {554},
pages = {256--275},
year = {2021},
url = {https://doi.org/10.1016/j.ins.2020.12.010},
doi = {10.1016/j.ins.2020.12.010},
timestamp = {Fri, 09 Apr 2021 18:25:48 +0200},
biburl = {https://dblp.org/rec/journals/isci/WangYLK21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Knee Point Identification Based on the Geometric Characteristic
Renzhi Chen+, Ke Li
Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'21), IEEE, p. 764–769, October, 2021 10.1109/SMC52423.2021.9658848 PDF | BiB
@inproceedings{Chen021,
author = {Renzhi Chen and
Ke Li},
title = {Knee Point Identification Based on the Geometric Characteristic},
booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
{SMC} 2021, Melbourne, Australia, October 17-20, 2021},
pages = {764--769},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SMC52423.2021.9658848},
doi = {10.1109/SMC52423.2021.9658848},
timestamp = {Tue, 11 Jan 2022 10:00:39 +0100},
biburl = {https://dblp.org/rec/conf/smc/Chen021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Large-Scale Evolutionary Optimization via Multi-Task Random Grouping
Phoenix Williams+, Ke Li
Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'21), IEEE, p. 778–783, October, 2021 10.1109/SMC52423.2021.9659276 PDF | BiB
@inproceedings{WilliamsLM21,
author = {Phoenix Neale Williams and
Ke Li and
Geyong Min},
title = {Large-Scale Evolutionary Optimization via Multi-Task Random Grouping},
booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
{SMC} 2021, Melbourne, Australia, October 17-20, 2021},
pages = {778--783},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SMC52423.2021.9659276},
doi = {10.1109/SMC52423.2021.9659276},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/conf/smc/WilliamsLM21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Transfer Bayesian Optimization for Expensive Black-Box Optimization in Dynamic Environment
Renzhi Chen+, Ke Li
Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'21), IEEE, p. 1374–1379, October, 2021 10.1109/SMC52423.2021.9659200 PDF | BiB
@inproceedings{ChenL21,
author = {Renzhi Chen and
Ke Li},
title = {Transfer Bayesian Optimization for Expensive Black-Box Optimization
in Dynamic Environment},
booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
{SMC} 2021, Melbourne, Australia, October 17-20, 2021},
pages = {1374--1379},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SMC52423.2021.9659200},
doi = {10.1109/SMC52423.2021.9659200},
timestamp = {Tue, 11 Jan 2022 10:00:39 +0100},
biburl = {https://dblp.org/rec/conf/smc/Chen021a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
ADMM-based OPF Problem Against Cyber Attacks in Smart Grid
Jiangjiao Xu+, Ke Li, Mohammad Abusara, Yan Zhang
Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics
(SMC'21), IEEE, p. 1418–1423, October, 2021 10.1109/SMC52423.2021.9658699 PDF | BiB
@inproceedings{XuLA021,
author = {Jiangjiao Xu and
Ke Li and
Mohammad Abusara and
Yan Zhang},
title = {ADMM-based {OPF} Problem Against Cyber Attacks in Smart Grid},
booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
{SMC} 2021, Melbourne, Australia, October 17-20, 2021},
pages = {1418--1423},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SMC52423.2021.9658699},
doi = {10.1109/SMC52423.2021.9658699},
timestamp = {Thu, 10 Mar 2022 11:05:49 +0100},
biburl = {https://dblp.org/rec/conf/smc/XuLA021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An Enhancement of the NSGA-II Reliability Optimization using Extended Kalman Filter Based Initialization
Savas Yuec+, Ke Li
Proc. of the 2021 20th UK Workshop on Computational Intelligence
(UKCI'21), Springer, p. 121–128, September, 2021 10.1007/978-3-030-87094-2_11 PDF | BiB
@inproceedings{YuceL21,
author = {Savas Yuce and
Ke Li},
title = {An Enhancement of the {NSGA-II} Reliability Optimization Using Extended
Kalman Filter Based Initialization},
booktitle = {Advances in Computational Intelligence Systems - Contributions Presented
at the 20th {UK} Workshop on Computational Intelligence, September
8-10, 2021, Aberystwyth, Wales, {UK}},
series = {Advances in Intelligent Systems and Computing},
volume = {1409},
pages = {121--128},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-87094-2\_11},
doi = {10.1007/978-3-030-87094-2\_11},
timestamp = {Thu, 16 Dec 2021 15:07:27 +0100},
biburl = {https://dblp.org/rec/conf/ukci/YuceL21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Empirical Studies on the Role of the Decision Maker in Interactive Evolutionary Multi-Objective Optimization
Guiyu Lai+, Minhui Liao, Ke Li
Proc. of the 2021 IEEE Congress on Evolutionary Computation
(CEC'21), IEEE, p. 1–8, June, 2021 10.1109/CEC45853.2021.9504980 PDF | BiB
@inproceedings{LaiL021,
author = {Guiyu Lai and
Minhui Liao and
Ke Li},
title = {Empirical Studies on the Role of the Decision Maker in Interactive
Evolutionary Multi-Objective Optimization},
booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2021, Krak{\'{o}}w,
Poland, June 28 - July 1, 2021},
pages = {185--192},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/CEC45853.2021.9504980},
doi = {10.1109/CEC45853.2021.9504980},
timestamp = {Thu, 12 Aug 2021 16:39:59 +0200},
biburl = {https://dblp.org/rec/conf/cec/LaiL021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Empirical Study of Correlations in the Fitness Landscapes of Combinatorial Optimization Problems
Longfei Zhang+, Ke Li, Shi Gu
Proc. of the 23th Annual Conference on Genetic and Evolutionary Computation
(GECCO’21), ACM, p. 247–248, July, 2021. 10.1145/3449726.3459528 PDF | BiB
@inproceedings{ZhangLG21,
author = {Longfei Zhang and
Ke Li and
Shi Gu},
title = {Empirical study of correlations in the fitness landscapes of combinatorial
optimization problems},
booktitle = {{GECCO} '21: Genetic and Evolutionary Computation Conference, Companion
Volume, Lille, France, July 10-14, 2021},
pages = {247--248},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3449726.3459528},
doi = {10.1145/3449726.3459528},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/conf/gecco/ZhangLG21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
Xinyu Shan+, Ke Li
Proc. of the 11th International Conference on Evolutionary Multi-Criterion Optimization
(EMO'21), Springer LNCS, volume 12654, p. 235–247, March, 2021 10.1007/978-3-030-72062-9_19 PDF | BiB
@inproceedings{ShanL21,
author = {Xinyu Shan and
Ke Li},
title = {An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-objective
Optimization},
booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference,
{EMO} 2021, Shenzhen, China, March 28-31, 2021, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {12654},
pages = {235--247},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-72062-9\_19},
doi = {10.1007/978-3-030-72062-9\_19},
timestamp = {Thu, 08 Apr 2021 15:51:58 +0200},
biburl = {https://dblp.org/rec/conf/emo/ShanL21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem
Jiangjiao Xu+, Ke Li, Mohammad Abusara
Proc. of the 11th International Conference on Evolutionary Multi-Criterion Optimization
(EMO'21), Springer LNCS, volume 12654, p. 684–696, March, 2021 10.1007/978-3-030-72062-9_54 PDF | BiB
@inproceedings{XuLA21,
author = {Jiangjiao Xu and
Ke Li and
Mohammad Abusara},
title = {Multi-objective Reinforcement Learning Based Multi-microgrid System
Optimisation Problem},
booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference,
{EMO} 2021, Shenzhen, China, March 28-31, 2021, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {12654},
pages = {684--696},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-72062-9\_54},
doi = {10.1007/978-3-030-72062-9\_54},
timestamp = {Mon, 12 Apr 2021 14:42:37 +0200},
biburl = {https://dblp.org/rec/conf/emo/XuLA21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Parallel Algorithms for Multiobjective Virtual Network Function Placement Problem
Joseph Billingsley+, Ke Li, Wang Miao, Geyong Min, Nektarios Georgalas
Proc. of the 11th International Conference on Evolutionary Multi-Criterion Optimization
(EMO'21), Springer LNCS, volume 12654, p. 708–720, March, 2021 10.1007/978-3-030-72062-9_56 PDF | BiB
@inproceedings{BillingsleyLMMG21,
author = {Joseph Billingsley and
Ke Li and
Wang Miao and
Geyong Min and
Nektarios Georgalas},
title = {Parallel Algorithms for the Multiobjective Virtual Network Function
Placement Problem},
booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference,
{EMO} 2021, Shenzhen, China, March 28-31, 2021, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {12654},
pages = {708--720},
publisher = {Springer},
year = {2021},EDITOR
url = {https://doi.org/10.1007/978-3-030-72062-9\_56},
doi = {10.1007/978-3-030-72062-9\_56},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/conf/emo/BillingsleyLMMG21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Points Ke Li, Minhui Liao+, Kalyanmoy Deb, Geyong Min, Xin Yao
IEEE Trans. Evolutionary Computation (TEVC), 24(6): 1078–1096, 2020. 10.1109/TEVC.2020.2987559 PDF |
Code | BiB
@article{LiLDMY20,
author = {Ke Li and
Minhui Liao and
Kalyanmoy Deb and
Geyong Min and
Xin Yao},
title = {Does Preference Always Help? {A} Holistic Study on Preference-Based
Evolutionary Multiobjective Optimization Using Reference Points},
journal = {{IEEE} Trans. Evol. Comput.},
volume = {24},
number = {6},
pages = {1078--1096},
year = {2020},
url = {https://doi.org/10.1109/TEVC.2020.2987559},
doi = {10.1109/TEVC.2020.2987559},
timestamp = {Thu, 17 Dec 2020 18:29:03 +0100},
biburl = {https://dblp.org/rec/journals/tec/LiLDMY20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms
Rong Tang, Ke Li, Wei Ding, Yuntao Wang, Huicheng Zhou, Guangtao Fu
Water Resources Management, 34: 1005–1020, 2020. 10.1007/s11269-020-02485-9 PDF | BiB
@article{tangLDWZF20,
title = {Reference point based multi-objective optimization of reservoir operation: a comparison of three algorithms},
author = {Tang, Rong and
Li, Ke and
Ding, Wei and
Wang, Yuntao and
Zhou, Huicheng and
Fu, Guangtao},
journal = {Water Resources Management},
volume = {34},
number = {3},
pages = {1005--1020},
year = {2020},
publisher = {Springer}
}
Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
Mengyuan Wu+, Ke Li, Sam Kwong, Qingfu Zhang
IEEE Trans. Cybernetics (TCYB), 50(2): 753–764, 2020. 10.1109/TCYB.2018.2872803 PDF |
Supp | Code | BiB
@article{WuLKZ20,
author = {Mengyuan Wu and
Ke Li and
Sam Kwong and
Qingfu Zhang},
title = {Evolutionary Many-Objective Optimization Based on Adversarial Decomposition},
journal = {{IEEE} Trans. Cybern.},
volume = {50},
number = {2},
pages = {753--764},
year = {2020},
url = {https://doi.org/10.1109/TCYB.2018.2872803},
doi = {10.1109/TCYB.2018.2872803},
timestamp = {Sat, 30 May 2020 19:51:36 +0200},
biburl = {https://dblp.org/rec/journals/tcyb/WuLKZ20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Performance Analysis of SDN and NFV enabled Mobile Cloud Computing
Joseph Billingsley+, Ke Li, Wang Miao, Geyong Min, Nektarios Georgalas
Proc. of the 2020 IEEE Global Communications Conference
(GLOBECOM'20), IEEE Press: p. 1–6, December, 2020 10.1109/GLOBECOM42002.2020.9322530 PDF | BiB
@inproceedings{BillingsleyMLMG20,
author = {Joseph Billingsley and
Wang Miao and
Ke Li and
Geyong Min and
Nektarios Georgalas},
title = {Performance Analysis of {SDN} and {NFV} enabled Mobile Cloud Computing},
booktitle = {{IEEE} Global Communications Conference, {GLOBECOM} 2020, Virtual
Event, Taiwan, December 7-11, 2020},
pages = {1--6},
publisher = {{IEEE}},
year = {2020},
url = {https://doi.org/10.1109/GLOBECOM42002.2020.9322530},
doi = {10.1109/GLOBECOM42002.2020.9322530},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/conf/globecom/BillingsleyMLMG20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Knee Point Identification Based on Voronoi Diagram
Haifeng Nie+, Huiru Gao+, Ke Li
Proc. of the 2020 IEEE Conference on Systems, Man and Cybernetics
(SMC'20), IEEE Press: p. 1–6, December, 2020 10.1109/SMC42975.2020.9283262 PDF | BiB
@inproceedings{NieGL20,
author = {Haifeng Nie and
Huiru Gao and
Ke Li},
title = {Knee Point Identification Based on Voronoi Diagram},
booktitle = {2020 {IEEE} International Conference on Systems, Man, and Cybernetics,
{SMC} 2020, Toronto, ON, Canada, October 11-14, 2020},
pages = {1081--1086},
publisher = {{IEEE}},
year = {2020},
url = {https://doi.org/10.1109/SMC42975.2020.9283262},
doi = {10.1109/SMC42975.2020.9283262},
timestamp = {Fri, 08 Jan 2021 11:20:37 +0100},
biburl = {https://dblp.org/rec/conf/smc/NieGL20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction Ke Li, Zilin Xiang+, Tao Chen, Kay Chen Tan
Proc. of the 35th IEEE/ACM International Conference on Automated Software Engineering
(ASE'20), IEEE Press: September, 2020 10.1145/3324884.3416617 PDF | Code | BiB
@inproceedings{LiXCT20,
author = {Ke Li and
Zilin Xiang and
Tao Chen and
Kay Chen Tan},
title = {BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project
Defect Prediction},
booktitle = {35th {IEEE/ACM} International Conference on Automated Software Engineering,
{ASE} 2020, Melbourne, Australia, September 21-25, 2020},
pages = {573--584},
publisher = {{IEEE}},
year = {2020},
url = {https://doi.org/10.1145/3324884.3416617},
doi = {10.1145/3324884.3416617},
timestamp = {Fri, 12 Feb 2021 13:04:43 +0100},
biburl = {https://dblp.org/rec/conf/kbse/LiXCT20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D
Lei Sun+, Ke Li
Proc. of the 16th International Conference on Parallel Problem Solving from Nature
(PPSN XVI), Springer LNCS, volume 12270, p. 271–284, Septermber, 2020. 10.1007/978-3-030-58115-2_19 PDF | BiB
@inproceedings{SunL20,
author = {Lei Sun and
Ke Li},
title = {Adaptive Operator Selection Based on Dynamic Thompson Sampling for
{MOEA/D}},
booktitle = {Parallel Problem Solving from Nature - {PPSN} {XVI} - 16th International
Conference, {PPSN} 2020, Leiden, The Netherlands, September 5-9, 2020,
Proceedings, Part {II}},
series = {Lecture Notes in Computer Science},
volume = {12270},
pages = {271--284},
publisher = {Springer},
year = {2020},
url = {https://doi.org/10.1007/978-3-030-58115-2\_19},
doi = {10.1007/978-3-030-58115-2\_19},
timestamp = {Sat, 19 Sep 2020 13:19:33 +0200},
biburl = {https://dblp.org/rec/conf/ppsn/SunL20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Surrogate Assisted Evolutionary Algorithm for Medium Scale Multi-Objective Optimisation Problems
Xiaoran Ruan+, Ke Li, Bilel Derbel, Arnaud Liefooghe
Proc. of the 22th Annual Conference on Genetic and Evolutionary Computation
(GECCO’20), ACM Press: p. 560–568, July, 2020. 10.1145/3377930.3390191 PDF | BiB
@inproceedings{RuanLDL20,
author = {Xiaoran Ruan and
Ke Li and
Bilel Derbel and
Arnaud Liefooghe},
title = {Surrogate assisted evolutionary algorithm for medium scale multi-objective
optimisation problems},
booktitle = {{GECCO} '20: Genetic and Evolutionary Computation Conference, Canc{\'{u}}n
Mexico, July 8-12, 2020},
pages = {560--568},
publisher = {{ACM}},
year = {2020},
url = {https://doi.org/10.1145/3377930.3390191},
doi = {10.1145/3377930.3390191},
timestamp = {Thu, 19 May 2022 17:23:34 +0200},
biburl = {https://dblp.org/rec/conf/gecco/RuanLDL20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Routing-Led Placement of VNFs in Arbitrary Networks
Joseph Billingsley+, Ke Li, Wang Miao, Geyong Min, Nektarios Georgalas
Proc. of the 2020 World Congress on Computational Intelligence
(WCCI'20), IEEE Press: p. 1–8, July, 2020. 10.1109/CEC48606.2020.9185531 PDF | BiB
@article{BillingsleyLMMG20,
author = {Joseph Billingsley and
Ke Li and
Wang Miao and
Geyong Min and
Nektarios Georgalas},
title = {Routing-Led Placement of VNFs in Arbitrary Networks},
journal = {CoRR},
volume = {abs/2001.11565},
year = {2020},
url = {https://arxiv.org/abs/2001.11565},
eprinttype = {arXiv},
eprint = {2001.11565},
timestamp = {Mon, 03 Feb 2020 11:21:05 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2001-11565.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning for Dynamic Expensive Multi-Objective Optimisation Problems
Xuezhou Fan, Ke Li, Kay Chen Tan
Proc. of the 2020 World Congress on Computational Intelligence
(WCCI'20), IEEE Press: p. 1–8, July, 2020. 10.1109/CEC48606.2020.9185522 PDF | BiB
@inproceedings{FanLT20,
author = {Xuezhou Fan and
Ke Li and
Kay Chen Tan},
title = {Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning
for Dynamic Expensive Multi-Objective Optimisation Problems},
booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2020, Glasgow,
United Kingdom, July 19-24, 2020},
pages = {1--8},
publisher = {{IEEE}},
year = {2020},
url = {https://doi.org/10.1109/CEC48606.2020.9185522},
doi = {10.1109/CEC48606.2020.9185522},
timestamp = {Fri, 11 Sep 2020 15:04:41 +0200},
biburl = {https://dblp.org/rec/conf/cec/FanLT20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
DeepSQLi: Deep Semantic Learning for Testing SQL Injection
Muyang Liu+, Ke Li, Tao Chen
Proc. of the ACM SIGSOFT 2020 International Symposium on Software Testing and Analysis
(ISSTA'20), ACM Press: p. 286–297, July, 2020. 10.1145/3395363.3397375 PDF |
Code | BiB
@inproceedings{LiuLC20,
author = {Muyang Liu and
Ke Li and
Tao Chen},
title = {DeepSQLi: deep semantic learning for testing {SQL} injection},
booktitle = {{ISSTA} '20: 29th {ACM} {SIGSOFT} International Symposium on Software
Testing and Analysis, Virtual Event, USA, July 18-22, 2020},
pages = {286--297},
publisher = {{ACM}},
year = {2020},
url = {https://doi.org/10.1145/3395363.3397375},
doi = {10.1145/3395363.3397375},
timestamp = {Wed, 15 Jul 2020 16:06:56 +0200},
biburl = {https://dblp.org/rec/conf/issta/Liu0020.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study Ke Li*, Zilin Xiang+*, Tao Chen*, Shuo Wang, Kay Chen Tan
Proc. of the 42nd International Conference on Software Engineering
(ICSE'20), ACM Press: p. 566–577, June, 2020. 10.1145/3377811.3380360 PDF |
Code | BiB
@inproceedings{LiX0WT20,
author = {Ke Li and
Zilin Xiang and
Tao Chen and
Shuo Wang and
Kay Chen Tan},
title = {Understanding the automated parameter optimization on transfer learning
for cross-project defect prediction: an empirical study},
booktitle = {{ICSE} '20: 42nd International Conference on Software Engineering,
Seoul, South Korea, 27 June - 19 July, 2020},
pages = {566--577},
publisher = {{ACM}},
year = {2020},
url = {https://doi.org/10.1145/3377811.3380360},
doi = {10.1145/3377811.3380360},
timestamp = {Tue, 12 Jan 2021 14:44:41 +0100},
biburl = {https://dblp.org/rec/conf/icse/LiX0WT20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang
Proc. of the 20th European Conference on Evolutionary Computation in Combinatorial Optimisation
(EvoCOP'20), Springer LNCS, volume 12102, p. 131–147, April, 2020. 10.1007/978-3-030-43680-3_9 PDF | BiB
@inproceedings{PruvostDLL020,
author = {Geoffrey Pruvost and
Bilel Derbel and
Arnaud Liefooghe and
Ke Li and
Qingfu Zhang},
title = {On the Combined Impact of Population Size and Sub-problem Selection
in {MOEA/D}},
booktitle = {Evolutionary Computation in Combinatorial Optimization - 20th European
Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain,
April 15-17, 2020, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {12102},
pages = {131--147},
publisher = {Springer},
year = {2020},
url = {https://doi.org/10.1007/978-3-030-43680-3\_9},
doi = {10.1007/978-3-030-43680-3\_9},
timestamp = {Sun, 25 Jul 2021 11:53:08 +0200},
biburl = {https://dblp.org/rec/conf/evoW/PruvostDLL020.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Learning to Decompose: A Paradigm for Decomposition-Based Multi-Objective Optimization
Mengyuan Wu+, Ke Li, Sam Kwong, Qingfu Zhang, Jun Zhang
IEEE Trans. Evolutionary Computation (TEVC), 23(3): 376–390, 2019. 10.1109/TEVC.2018.2865931 PDF | Supp | Code | BiB
@article{WuLKZZ19,
author = {Mengyuan Wu and
Ke Li and
Sam Kwong and
Qingfu Zhang and
Jun Zhang},
title = {Learning to Decompose: {A} Paradigm for Decomposition-Based Multiobjective
Optimization},
journal = {{IEEE} Trans. Evol. Comput.},
volume = {23},
number = {3},
pages = {376--390},
year = {2019},
url = {https://doi.org/10.1109/TEVC.2018.2865931},
doi = {10.1109/TEVC.2018.2865931},
timestamp = {Tue, 12 May 2020 16:51:10 +0200},
biburl = {https://dblp.org/rec/journals/tec/WuLKZZ19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions Ke Li*, Renzhi Chen*+, Dragan Savic, Xin Yao
IEEE Trans. Fuzzy Systems (TFS), 27(5): 845–860, 2019. 10.1109/TFUZZ.2018.2880700 PDF |
Supp | Code | BiB
@article{LiCSY19,
author = {Ke Li and
Renzhi Chen and
Dragan A. Savic and
Xin Yao},
title = {Interactive Decomposition Multiobjective Optimization Via Progressively
Learned Value Functions},
journal = {{IEEE} Trans. Fuzzy Syst.},
volume = {27},
number = {5},
pages = {849--860},
year = {2019},
url = {https://doi.org/10.1109/TFUZZ.2018.2880700},
doi = {10.1109/TFUZZ.2018.2880700},
timestamp = {Tue, 12 May 2020 16:52:42 +0200},
biburl = {https://dblp.org/rec/journals/tfs/LiCSY19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization Ke Li*, Renzhi Chen*+, Guangtao Fu, Xin Yao
IEEE Trans. Evolutionary Computation (TEVC), 23(2): 303–315, 2019. 10.1109/TEVC.2018.2855411 PDF |
Supp |
Code | BiB
@article{LiCFY19,
author = {Ke Li and
Renzhi Chen and
Guangtao Fu and
Xin Yao},
title = {Two-Archive Evolutionary Algorithm for Constrained Multiobjective
Optimization},
journal = {{IEEE} Trans. Evol. Comput.},
volume = {23},
number = {2},
pages = {303--315},
year = {2019},
url = {https://doi.org/10.1109/TEVC.2018.2855411},
doi = {10.1109/TEVC.2018.2855411},
timestamp = {Tue, 12 May 2020 16:51:04 +0200},
biburl = {https://dblp.org/rec/journals/tec/LiCFY19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Knee-Point-Based Evolutionary Algorithm Using Weighted Subpopulation for Many-Objective Optimization
Juan Zou, Chunhui Ji, Shengxiang Yang, Yuping Zhang, Jinhua Zheng, Ke Li
Swarm and Evolutionary Computation, 47: 33–43, 2019. 10.1016/j.swevo.2019.02.001 PDF | BiB
@article{ZouJYZZL19,
author = {Juan Zou and
Chunhui Ji and
Shengxiang Yang and
Yuping Zhang and
Jinhua Zheng and
Ke Li},
title = {A Knee-Point-Based Evolutionary Algorithm Using Weighted Subpopulation for Many-Objective Optimization},
journal = {Swarm and Evolutionary Computation},
year = {2019},
note = {accepted for publication}
}
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution Ke Li, Zilin Xiang+, Kay Chen Tan
Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC'19), IEEE Press: p. 1988–1995, June, 2019. 10.1109/CEC.2019.8789984 PDF | Supp | BiB
@article{LiXT19,
author = {Ke Li and
Zilin Xiang and
Kay Chen Tan},
title = {Which Surrogate Works for Empirical Performance Modelling? {A} Case
Study with Differential Evolution},
journal = {CoRR},
volume = {abs/1901.11120},
year = {2019},
url = {http://arxiv.org/abs/1901.11120},
eprinttype = {arXiv},
eprint = {1901.11120},
timestamp = {Mon, 16 Mar 2020 17:55:51 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1901-11120.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons
Huiru Gao+, Haifeng Nie+, Ke Li
Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC'19), IEEE Press: p. 1750–1757, June, 2019. 10.1109/CEC.2019.8790298 PDF | Supp | BiB
@article{GaoNL19,
author = {Huiru Gao and
Haifeng Nie and
Ke Li},
title = {Visualisation of Pareto Front Approximation: {A} Short Survey and
Empirical Comparisons},
journal = {CoRR},
volume = {abs/1903.01768},
year = {2019},
url = {http://arxiv.org/abs/1903.01768},
eprinttype = {arXiv},
eprint = {1903.01768},
timestamp = {Fri, 08 Jan 2021 11:20:34 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1903-01768.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Decomposition Multi-Objective Optimisation: Current Developments and Future Opportunities Ke Li, Qingfu Zhang
Proc. of the 21th Annual Conference on Genetic and Evolutionary Computation (GECCO’19): ACM Press: p. 1002–1031, July 2019. 10.1145/3319619.3323369 Slides | BiB
@inproceedings{LiZ19,
author = {Ke Li and
Qingfu Zhang},
title = {Decomposition multi-objective optimisation: current developments and
future opportunities},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
Companion, {GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
pages = {1002--1031},
publisher = {{ACM}},
year = {2019},
url = {https://doi.org/10.1145/3319619.3323369},
doi = {10.1145/3319619.3323369},
timestamp = {Mon, 15 Jul 2019 16:26:46 +0200},
biburl = {https://dblp.org/rec/conf/gecco/LiZ19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Security Testing of Web Applications: A Search-Based Approach for Detecting SQL Injection Vulnerabilities
Muyang Liu+, Ke Li, Tao Chen
Proc. of the 21th Annual Conference on Genetic and Evolutionary Computation (GECCO’19), ACM Press: p. 417–418, July 2019. 10.1145/3319619.3322026 PDF | BiB
@inproceedings{LiuLC19,
author = {Muyang Liu and
Ke Li and
Tao Chen},
title = {Security testing of web applications: a search-based approach for
detecting {SQL} injection vulnerabilities},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
Companion, {GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
pages = {417--418},
publisher = {{ACM}},
year = {2019},
url = {https://doi.org/10.1145/3319619.3322026},
doi = {10.1145/3319619.3322026},
timestamp = {Wed, 08 Jan 2020 08:56:46 +0100},
biburl = {https://dblp.org/rec/conf/gecco/LiuLC19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Progressive Preference Learning: Proof-of-Principle Results in MOEA/D Ke Li
Proc. of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO’19), Springer LNCS, volume 11411, p. 631–643, March 2019. 10.1007/978-3-030-12598-1_50 PDF |
Supp | Code | BiB
@inproceedings{Li19,
author = {Ke Li},
title = {Progressive Preference Learning: Proof-of-Principle Results in {MOEA/D}},
booktitle = {EMO'19: Proc. of the 10th International Conference Evolutionary Multi-Criterion Optimization},
pages = {631--643},
year = {2019},
url = {https://doi.org/10.1007/978-3-030-12598-1\_50},
doi = {10.1007/978-3-030-12598-1\_50},
timestamp = {Thu, 28 Feb 2019 14:53:34 +0100},
biburl = {https://dblp.org/rec/bib/conf/emo/Li19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Formal Model for Multi-objective Optimisation of NFV Placement
Joseph Billingsley, Ke Li, Wang Miao, Geyong Min, N. Georgalas
Proc. of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO’19), Springer LNCS, volume 11411, p. 529–540, March 2019. 10.1007/978-3-030-12598-1_42 PDF | BiB
@inproceedings{BillingsleyLMMG19,
author = {Joseph Billingsley and
Ke Li and
Wang Miao and
Geyong Min and
Nektarios Georgalas},
title = {A Formal Model for Multi-objective Optimisation of Network Function
Virtualisation Placement},
booktitle = {Evolutionary Multi-Criterion Optimization - 10th International Conference,
{EMO} 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {11411},
pages = {529--540},
publisher = {Springer},
year = {2019},
url = {https://doi.org/10.1007/978-3-030-12598-1\_42},
doi = {10.1007/978-3-030-12598-1\_42},
timestamp = {Fri, 26 Feb 2021 09:21:56 +0100},
biburl = {https://dblp.org/rec/conf/emo/BillingsleyLMMG19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
R-Metric: Evaluating the Performance of Preference-Based Evolutionary Multi-Objective Optimization Using Reference Points Ke Li, Kalyanmoy Deb, Xin Yao
IEEE Trans. Evolutionary Computation (TEVC), 22(6): 821–835, 2018. 10.1109/TEVC.2017.2737781 PDF |
Supp |
Code | BiB
@article{LiDY18,
author = {Ke Li and
Kalyanmoy Deb and
Xin Yao},
title = {R-Metric: Evaluating the Performance of Preference-Based Evolutionary
Multiobjective Optimization Using Reference Points},
journal = {{IEEE} Trans. Evol. Comput.},
volume = {22},
number = {6},
pages = {821--835},
year = {2018},
url = {https://doi.org/10.1109/TEVC.2017.2737781},
doi = {10.1109/TEVC.2017.2737781},
timestamp = {Tue, 12 May 2020 16:50:45 +0200},
biburl = {https://dblp.org/rec/journals/tec/LiDY18.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Integration of Preferences in Decomposition Multiobjective Optimization Ke Li*, Renzhi Chen*+, Geyong Min, Xin Yao
IEEE Trans. Cybernetics (TCYB), 48(12): 3359–3370, 2018. 10.1109/TCYB.2018.2859363 PDF |
Supp |
Code | BiB
@article{LiCMY18,
author = {Ke Li and
Renzhi Chen and
Geyong Min and
Xin Yao},
title = {Integration of Preferences in Decomposition Multiobjective Optimization},
journal = {{IEEE} Trans. Cybernetics},
volume = {48},
number = {12},
pages = {3359--3370},
year = {2018},
url = {https://doi.org/10.1109/TCYB.2018.2859363},
doi = {10.1109/TCYB.2018.2859363},
timestamp = {Sun, 23 Dec 2018 17:21:04 +0100},
biburl = {https://dblp.org/rec/bib/journals/tcyb/LiCMY18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Evolutionary Multiobjective Optimization-Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection
Ran Cheng, Miqing Li, Ke Li, Xin Yao
IEEE Trans. Evolutionary Computation (TEVC), 22(5): 692–706, 2018. 10.1109/TEVC.2017.2744328 PDF | BiB
@article{ChengLLY18,
author = {Ran Cheng and
Miqing Li and
Ke Li and
Xin Yao},
title = {Evolutionary Multiobjective Optimization-Based Multimodal Optimization:
Fitness Landscape Approximation and Peak Detection},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {22},
number = {5},
pages = {692--706},
year = {2018},
url = {https://doi.org/10.1109/TEVC.2017.2744328},
doi = {10.1109/TEVC.2017.2744328},
timestamp = {Sun, 23 Dec 2018 17:21:04 +0100},
biburl = {https://dblp.org/rec/bib/journals/tec/ChengLLY18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software at Runtime
Tao Chen, Ke Li, Rami Bahsoon, Xin Yao
ACM Trans. Software Engineering and Methodology (TOSEM), 27(2): 1–50, 2018. 10.1145/3204459 PDF |
Code | BiB
@article{ChenLBY18,
author = {Tao Chen and
Ke Li and
Rami Bahsoon and
Xin Yao},
title = {{FEMOSAA:} Feature-Guided and Knee-Driven Multi-Objective Optimization
for Self-Adaptive Software},
journal = {{ACM} Trans. Softw. Eng. Methodol.},
volume = {27},
number = {2},
pages = {5:1--5:50},
year = {2018},
url = {https://doi.org/10.1145/3204459},
doi = {10.1145/3204459},
timestamp = {Wed, 21 Nov 2018 12:44:28 +0100},
biburl = {https://dblp.org/rec/bib/journals/tosem/ChenLBY18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Dynamic Multi-Objectives Optimization with a Changing Number of Objectives Ke Li*, Renzhi Chen*+, Xin Yao,
IEEE Trans. Evolutionary Computation (TEVC), 21(1): 157–171, 2018. 10.1109/TEVC.2017.2669638 PDF |
Supp |
Code | BiB
@article{ChenLY18,
author = {Renzhi Chen and
Ke Li and
Xin Yao},
title = {Dynamic Multiobjectives Optimization With a Changing Number of Objectives},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {22},
number = {1},
pages = {157--171},
year = {2018},
url = {https://doi.org/10.1109/TEVC.2017.2669638},
doi = {10.1109/TEVC.2017.2669638},
timestamp = {Wed, 04 Jul 2018 13:22:50 +0200},
biburl = {https://dblp.org/rec/bib/journals/tec/ChenLY18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Multi-Tenant Cloud Service Composition using Evolutionary Optimization
Satish Kumar, Rami Bahsoon, Tao Chen, Ke Li, R. Buyya,
Proc. of the 24th International Conference on Parallel and Distributed Systems
(ICPADS’18), IEEE Press: p. 972–979, December 2018. 10.1109/PADSW.2018.8644640 PDF | BiB
@inproceedings{KumarBCLB18,
author = {Satish Kumar and
Rami Bahsoon and
Tao Chen and
Ke Li and
Rajkumar Buyya},
title = {Multi-Tenant Cloud Service Composition Using Evolutionary Optimization},
booktitle = {ICPADS: Proc. of the 24th {IEEE} International Conference on Parallel and Distributed Systems},
pages = {972--979},
year = {2018},
url = {https://doi.org/10.1109/PADSW.2018.8644640},
doi = {10.1109/PADSW.2018.8644640},
timestamp = {Tue, 26 Feb 2019 16:36:22 +0100},
biburl = {https://dblp.org/rec/bib/conf/icpads/KumarBCLB18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Decomposition Multi-Objective Optimisation: Current Developments and Future Opportunities Ke Li, Qingfu Zhang
Proc. of the 20th Annual Conference on Genetic and Evolutionary Computation (GECCO’18), ACM Press: p. 903–936, July 2018. 10.1145/3205651.3207856 Slides | BiB
@inproceedings{LiZ18,
author = {Ke Li and
Qingfu Zhang},
title = {Decomposition multi-objective optimisation: current developments and
future opportunities},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
Companion, {GECCO} 2018, Kyoto, Japan, July 15-19, 2018},
pages = {907--936},
publisher = {{ACM}},
year = {2018},
url = {https://doi.org/10.1145/3205651.3207856},
doi = {10.1145/3205651.3207856},
timestamp = {Fri, 27 Dec 2019 21:30:09 +0100},
biburl = {https://dblp.org/rec/conf/gecco/0001Z18.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{LiZ18,
author = {Ke Li and
Qingfu Zhang},
title = {Decomposition multi-objective optimisation: current developments and
future opportunities},
booktitle = {GECCO'18: Proc. of the 20th Genetic and Evolutionary Computation Conference
Companion},
pages = {907--936},
year = {2018},
url = {https://doi.org/10.1145/3205651.3207856},
doi = {10.1145/3205651.3207856},
timestamp = {Wed, 21 Nov 2018 12:43:54 +0100},
biburl = {https://dblp.org/rec/bib/conf/gecco/0001Z18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Efficient Non-domination Level Update Method for Steady-State Evolutionary Multiobjective Optimization Ke Li, Kalyanmoy Deb, Qingfu Zhang, Qiang Zhang
IEEE Trans. Cybernetics (TCYB), 47(9): 2838–2849, 2017. 10.1109/TCYB.2016.2621008 PDF |
Supp |
Code | BiB
@article{LiDZZ17,
author = {Ke Li and
Kalyanmoy Deb and
Qingfu Zhang and
Qiang Zhang},
title = {Efficient Nondomination Level Update Method for Steady-State Evolutionary
Multiobjective Optimization},
journal = {{IEEE} Trans. Cybernetics},
volume = {47},
number = {9},
pages = {2838--2849},
year = {2017},
url = {https://doi.org/10.1109/TCYB.2016.2621008},
doi = {10.1109/TCYB.2016.2621008},
timestamp = {Wed, 14 Nov 2018 10:31:31 +0100},
biburl = {https://dblp.org/rec/bib/journals/tcyb/LiDZZ17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization
Mengyuan Wu+, Ke Li, Sam Kwong, Yu Zhou, Qingfu Zhang
IEEE Trans. Evolutionary Computation (TEVC), 21(4): 554–568, 2017. 10.1109/TEVC.2017.2656922 PDF | Supp |
Code | BiB
@article{WuLKZZ17,
author = {Mengyuan Wu and
Ke Li and
Sam Kwong and
Yu Zhou and
Qingfu Zhang},
title = {Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective
Optimization},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {21},
number = {4},
pages = {554--568},
year = {2017},
url = {https://doi.org/10.1109/TEVC.2017.2656922},
doi = {10.1109/TEVC.2017.2656922},
timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
biburl = {https://dblp.org/rec/bib/journals/tec/WuLKZZ17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Recent advances in semantic computing and personalization
Haoran Xie, Fu Lee Wang, Xudong Mao, Ke Li, Qing Li, Handing Wang
Neurocomputing (NEUCOM). 254: 1–2, 2017. 10.1016/j.neucom.2017.02.073 PDF | BiB
@article{XieWMLLW17,
author = {Haoran Xie and
Fu Lee Wang and
Xudong Mao and
Ke Li and
Qing Li and
Handing Wang},
title = {Recent advances in semantic computing and personalization},
journal = {Neurocomputing},
volume = {254},
pages = {1--2},
year = {2017},
url = {https://doi.org/10.1016/j.neucom.2017.02.073},
doi = {10.1016/j.neucom.2017.02.073},
timestamp = {Fri, 30 Nov 2018 13:23:21 +0100},
biburl = {https://dblp.org/rec/bib/journals/ijon/XieWMLLW17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results Ke Li, Kalyamoy Deb, Tolga Altinoz, Xin Yao
Proc. of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO’17), Springer LNCS, volume 10173, p. 390–405, March 2017. 10.1007/978-3-319-54157-0_27 PDF | BiB
@inproceedings{LiDAY17,
author = {Ke Li and
Kalyanmoy Deb and
Okkes Tolga Altin{\"{o}}z and
Xin Yao},
title = {Empirical Investigations of Reference Point Based Methods When Facing
a Massively Large Number of Objectives: First Results},
booktitle = {EMO'17: Proc. of the 9th International Conference Evolutionary Multi-Criterion Optimization},
pages = {390--405},
year = {2017},
url = {https://doi.org/10.1007/978-3-319-54157-0\_27},
doi = {10.1007/978-3-319-54157-0\_27},
timestamp = {Wed, 14 Nov 2018 10:59:29 +0100},
biburl = {https://dblp.org/rec/bib/conf/emo/LiDAY17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Adaptive weights generation for decomposition-based multi-objective optimization using Gaussian process regression
Mengyuan Wu, Sam Kwong, Yuheng Jia, Ke Li, Qingfu Zhang
Proc. of the 19th Annual Conference on Genetic and Evolutionary Computation (GECCO’17), ACM Press: p. 641–648, July 2017. 10.1145/3071178.3071339 PDF | BiB
@inproceedings{WuKJLZ17,
author = {Mengyuan Wu and
Sam Kwong and
Yuheng Jia and
Ke Li and
Qingfu Zhang},
title = {Adaptive weights generation for decomposition-based multi-objective
optimization using Gaussian process regression},
booktitle = {GECCO'17: Proc. of the 19th Genetic and Evolutionary Computation Conference},
pages = {641--648},
year = {2017},
url = {https://doi.org/10.1145/3071178.3071339},
doi = {10.1145/3071178.3071339},
timestamp = {Tue, 06 Nov 2018 11:06:40 +0100},
biburl = {https://dblp.org/rec/bib/conf/gecco/WuKJLZ17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Personalized Search for Social Media via Dominating Verbal Context
Haoran Xie, Xiaodong Li, Tao Wang, Li Chen, Ke Li, Fu Lee Wang, Yi Cai, Qing Li, Huaqing Min
Neurocomputing (NEUCOM). 172: 27–37, 2016. 10.1016/j.neucom.2014.12.109 PDF | BiB
@article{XieLWCLWCLM16,
author = {Haoran Xie and
Xiaodong Li and
Tao Wang and
Li Chen and
Ke Li and
Fu Lee Wang and
Yi Cai and
Qing Li and
Huaqing Min},
title = {Personalized search for social media via dominating verbal context},
journal = {Neurocomputing},
volume = {172},
pages = {27--37},
year = {2016},
url = {https://doi.org/10.1016/j.neucom.2014.12.109},
doi = {10.1016/j.neucom.2014.12.109},
timestamp = {Wed, 14 Nov 2018 10:26:17 +0100},
biburl = {https://dblp.org/rec/bib/journals/ijon/XieLWCLWCLM16},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Variable Interactions in Multi-Objective Optimization Problems Ke Li, Mohammad Nabi Omidvar, Kalyanmoy Deb, Xin Yao
Proc. of the 14th International Conference on Parallel Problem Solving from Nature (PPSN’16), Springer LNCS, volume 9921, p. 399–409, September 2016. 10.1007/978-3-319-45823-6_37 PDF | BiB
@inproceedings{LiODY16,
author = {Ke Li and
Mohammad Nabi Omidvar and
Kalyanmoy Deb and
Xin Yao},
title = {Variable Interaction in Multi-objective Optimization Problems},
booktitle = {Parallel Problem Solving from Nature - {PPSN} {XIV} - 14th International
Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
series = {Lecture Notes in Computer Science},
volume = {9921},
pages = {399--409},
publisher = {Springer},
year = {2016},
url = {https://doi.org/10.1007/978-3-319-45823-6\_37},
doi = {10.1007/978-3-319-45823-6\_37},
timestamp = {Wed, 19 Jan 2022 09:30:47 +0100},
biburl = {https://dblp.org/rec/conf/ppsn/LiODY16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition Ke Li, Kalyanmoy Deb, Qingfu Zhang, Sam Kwong
IEEE Trans. Evolutionary Computation (TEVC), 19(5): 694–716, 2015. 10.1109/TEVC.2014.2373386 PDF |
Code | BiB
@article{LiDZK15,
author = {Ke Li and
Kalyanmoy Deb and
Qingfu Zhang and
Sam Kwong},
title = {An Evolutionary Many-Objective Optimization Algorithm Based on Dominance
and Decomposition},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {19},
number = {5},
pages = {694--716},
year = {2015},
url = {https://doi.org/10.1109/TEVC.2014.2373386},
doi = {10.1109/TEVC.2014.2373386},
timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
biburl = {https://dblp.org/rec/bib/journals/tec/LiDZK15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Interrelationship-Based Selection for Decomposition Multiobjective Optimization Ke Li, Sam Kwong, Qingfu Zhang, Kalyanmoy Deb
IEEE Trans. Cybernetics (TCYB), 45(10): 2076–2088, 2015 10.1109/TCYB.2014.2365354 PDF |
PDF |
Code | BiB
@article{LiKZD15,
author = {Ke Li and
Sam Kwong and
Qingfu Zhang and
Kalyanmoy Deb},
title = {Interrelationship-Based Selection for Decomposition Multiobjective
Optimization},
journal = {{IEEE} Trans. Cybernetics},
volume = {45},
number = {10},
pages = {2076--2088},
year = {2015},
url = {https://doi.org/10.1109/TCYB.2014.2365354},
doi = {10.1109/TCYB.2014.2365354},
timestamp = {Wed, 14 Nov 2018 10:31:35 +0100},
biburl = {https://dblp.org/rec/bib/journals/tcyb/LiKZD15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Dual-Population Paradigm for Evolutionary Multiobjective Optimization Ke Li, Sam Kwong, Kalyanmoy Deb
Information Sciences (INS), 309: 50–72, 2015. 10.1016/j.ins.2015.03.002 PDF |
Code | BiB
@article{LiKD15,
author = {Ke Li and
Sam Kwong and
Kalyanmoy Deb},
title = {A dual-population paradigm for evolutionary multiobjective optimization},
journal = {Inf. Sci.},
volume = {309},
pages = {50--72},
year = {2015},
url = {https://doi.org/10.1016/j.ins.2015.03.002},
doi = {10.1016/j.ins.2015.03.002},
timestamp = {Wed, 14 Nov 2018 10:27:37 +0100},
biburl = {https://dblp.org/rec/bib/journals/isci/LiKD15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Class-Specific Soft Voting based Multiple Extreme Learning Machines Ensemble
Jingjing Cao, Sam Kwong, Ran Wang, Xiaodong Li, Ke Li, Xiangfei Kong
Neurocomputing (NEUCOM). 149: 275–284, 2015. 10.1016/j.neucom.2014.02.072 PDF | BiB
@article{CaoKWLLK15,
author = {Jingjing Cao and
Sam Kwong and
Ran Wang and
Xiaodong Li and
Ke Li and
Xiangfei Kong},
title = {Class-specific soft voting based multiple extreme learning machines
ensemble},
journal = {Neurocomputing},
volume = {149},
pages = {275--284},
year = {2015},
url = {https://doi.org/10.1016/j.neucom.2014.02.072},
doi = {10.1016/j.neucom.2014.02.072},
timestamp = {Wed, 14 Nov 2018 10:26:17 +0100},
biburl = {https://dblp.org/rec/bib/journals/ijon/CaoKWLLK15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Two-Level Stable Matching-Based Selection in MOEA/D
Mengyuan Wu, Sam Kwong, Qingfu Zhang, Ke Li, Ran Wang, Bo Liu
Proc. of 2015 IEEE Conference on Systems, Mans and Cybernetics (SMC’15), IEEE Press: p. 1720–1725, October 2015. 10.1109/SMC.2015.302 PDF |
Code | BiB
@inproceedings{WuKZLWL15,
author = {Mengyuan Wu and
Sam Kwong and
Qingfu Zhang and
Ke Li and
Ran Wang and
Bo Liu},
title = {Two-Level Stable Matching-Based Selection in {MOEA/D}},
booktitle = {2015 {IEEE} International Conference on Systems, Man, and Cybernetics,
Kowloon Tong, Hong Kong, October 9-12, 2015},
pages = {1720--1725},
publisher = {{IEEE}},
year = {2015},
url = {https://doi.org/10.1109/SMC.2015.302},
doi = {10.1109/SMC.2015.302},
timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
biburl = {https://dblp.org/rec/conf/smc/WuKZLWL15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Evolutionary Multiobjective Optimization with Hybrid Selection Principles Ke Li, Kalyanmoy Deb, Qingfu Zhang
Proc. of 2015 IEEE Congress on Evolutionary Computation (CEC’15), IEEE Press: p. 900–907, May 2015. 10.1109/CEC.2015.7256986 PDF |
Code | BiB
@inproceedings{LiDZ15,
author = {Ke Li and
Kalyanmoy Deb and
Qingfu Zhang},
title = {Evolutionary multiobjective optimization with hybrid selection principles},
booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2015, Sendai, Japan,
May 25-28, 2015},
pages = {900--907},
publisher = {{IEEE}},
year = {2015},
url = {https://doi.org/10.1109/CEC.2015.7256986},
doi = {10.1109/CEC.2015.7256986},
timestamp = {Wed, 16 Oct 2019 14:14:52 +0200},
biburl = {https://dblp.org/rec/conf/cec/LiDZ15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{LiZKLW14,
author = {Ke Li and
Qingfu Zhang and
Sam Kwong and
Miqing Li and
Ran Wang},
title = {Stable Matching-Based Selection in Evolutionary Multiobjective Optimization},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {18},
number = {6},
pages = {909--923},
year = {2014},
url = {https://doi.org/10.1109/TEVC.2013.2293776},
doi = {10.1109/TEVC.2013.2293776},
timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
biburl = {https://dblp.org/rec/bib/journals/tec/LiZKLW14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Stable Matching Based Selection in Evolutionary Multiobjective Optimization Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li, Ran Wang
IEEE Trans. Evolutionary Computation (TEVC). 18(6): 909–923, 2014. 10.1109/TEVC.2013.2293776 PDF |
Code | BiB
@article{LiZKLW14,
author = {Ke Li and
Qingfu Zhang and
Sam Kwong and
Miqing Li and
Ran Wang},
title = {Stable Matching-Based Selection in Evolutionary Multiobjective Optimization},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {18},
number = {6},
pages = {909--923},
year = {2014},
url = {https://doi.org/10.1109/TEVC.2013.2293776},
doi = {10.1109/TEVC.2013.2293776},
timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
biburl = {https://dblp.org/rec/bib/journals/tec/LiZKLW14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Adaptive Operator Selection with Bandits for Multiobjective Evolutionary Algorithm Based on Decomposition Ke Li, Álvaro Fialho, Sam Kwong, Qingfu Zhang
IEEE Trans. Evolutionary Computation (TEVC). 18(1): 114–130, 2014. 10.1109/TEVC.2013.2239648 PDF |
Supp |
Code | BiB
@article{LiFKZ14,
author = {Ke Li and
{\'{A}}lvaro Fialho and
Sam Kwong and
Qingfu Zhang},
title = {Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary
Algorithm Based on Decomposition},
journal = {{IEEE} Trans. Evolutionary Computation},
volume = {18},
number = {1},
pages = {114--130},
year = {2014},
url = {https://doi.org/10.1109/TEVC.2013.2239648},
doi = {10.1109/TEVC.2013.2239648},
timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
biburl = {https://dblp.org/rec/bib/journals/tec/LiFKZ14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A General Framework for Evolutionary Multiobjective Optimization via Manifold Learning Ke Li, Sam Kwong
Neurocomputing (NEUCOM). 146: 65–74, 2014. 10.1016/j.neucom.2014.03.070 PDF | BiB
@article{LiK14,
author = {Ke Li and
Sam Kwong},
title = {A general framework for evolutionary multiobjective optimization via
manifold learning},
journal = {Neurocomputing},
volume = {146},
pages = {65--74},
year = {2014},
url = {https://doi.org/10.1016/j.neucom.2014.03.070},
doi = {10.1016/j.neucom.2014.03.070},
timestamp = {Wed, 14 Nov 2018 10:26:16 +0100},
biburl = {https://dblp.org/rec/bib/journals/ijon/LiK14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Evolutionary Algorithms with Segment-based Search for Multiobjective Optimization Problems
Miqing Li, Shengxiang Yang, Ke Li, Xiaohui Liu
IEEE Trans. Cybernetics (TCYB). 44(8): 1295–1313, 2014. 10.1109/TCYB.2013.2282503 PDF | BiB
@article{LiYLL14,
author = {Miqing Li and
Shengxiang Yang and
Ke Li and
Xiaohui Liu},
title = {Evolutionary Algorithms With Segment-Based Search for Multiobjective
Optimization Problems},
journal = {{IEEE} Trans. Cybernetics},
volume = {44},
number = {8},
pages = {1295--1313},
year = {2014},
url = {https://doi.org/10.1109/TCYB.2013.2282503},
doi = {10.1109/TCYB.2013.2282503},
timestamp = {Wed, 14 Nov 2018 10:31:34 +0100},
biburl = {https://dblp.org/rec/bib/journals/tcyb/LiYLL14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An Indicator-based Selection Multi-Objective Evolutionary Algorithm with Preference for Multi-Class Ensemble
Jingjing Cao, Sam Kwong, Ran Wang, Ke Li
Proc. of 2014 International Conference on Machine Learning and Cybernetics (ICMLC’12), IEEE Press: p. 147–152, July 2014. 10.1109/ICMLC.2014.7009108 PDF | BiB
@inproceedings{CaoKWL14,
author = {Jingjing Cao and
Sam Kwong and
Ran Wang and
Ke Li},
title = {{AN} indicator-based selection multi-objective evolutionary algorithm
with preference for multi-class ensemble},
booktitle = {ICMLC'14: Proc. of the 2014 International Conference on Machine Learning and Cybernetics},
pages = {147--152},
year = {2014},
url = {https://doi.org/10.1109/ICMLC.2014.7009108},
doi = {10.1109/ICMLC.2014.7009108},
timestamp = {Mon, 29 Jan 2018 16:21:37 +0100},
biburl = {https://dblp.org/rec/bib/conf/icmlc/CaoKWL14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{LiKWTM13,
author = {Ke Li and
Sam Kwong and
Ran Wang and
Kit{-}Sang Tang and
Kim{-}Fung Man},
title = {Learning paradigm based on jumping genes: {A} general framework for
enhancing exploration in evolutionary multiobjective optimization},
journal = {Inf. Sci.},
volume = {226},
pages = {1--22},
year = {2013},
url = {https://doi.org/10.1016/j.ins.2012.11.002},
doi = {10.1016/j.ins.2012.11.002},
timestamp = {Wed, 14 Nov 2018 10:27:36 +0100},
biburl = {https://dblp.org/rec/bib/journals/isci/LiKWTM13},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Learning Paradigm Based on Jumping Genes: A General Framework for Enhancing Exploration in Evolutionary Multiobjective Optimization Ke Li, Sam Kwong, Ran Wang, Kit-Sang Tang, Kim-Fung Man
Information Sciences (INS), 226: 1–22, 2013. 10.1016/j.ins.2012.11.002 PDF |
Code | BiB
@article{LiKWTM13,
author = {Ke Li and
Sam Kwong and
Ran Wang and
Kit{-}Sang Tang and
Kim{-}Fung Man},
title = {Learning paradigm based on jumping genes: {A} general framework for
enhancing exploration in evolutionary multiobjective optimization},
journal = {Inf. Sci.},
volume = {226},
pages = {1--22},
year = {2013},
url = {https://doi.org/10.1016/j.ins.2012.11.002},
doi = {10.1016/j.ins.2012.11.002},
timestamp = {Wed, 14 Nov 2018 10:27:36 +0100},
biburl = {https://dblp.org/rec/bib/journals/isci/LiKWTM13},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Evolving Extreme Learning Machine Paradigm with Adaptive Operator Selection and Parameter Control Ke Li, Ran Wang, Sam Kwong, Jingjing Cao
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS). 21(supp02): 143–154, 2013. 10.1142/S0218488513400229 PDF |
Code | BiB
@ARTICLE{IJUFKS13,
author = {Ke Li and
Ran Wang and
Sam Kwong and
Jingjing Cao},
title = {Evolving Extreme Learning Machine Paradigm with Adaptive Operator
Selection and Parameter Control},
journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based
Systems},
year = {2013},
volume = {21},
pages = {143-154}
}
Achieving Balance Between Proximity and Diversity in Multi-objective Evolutionary Algorithm Ke Li, Sam Kwong, Jingjing Cao, Miqing Li, Jinhua Zheng, Ruimin Shen
Information Sciences (INS), 182(1): 220–242, 2012. 10.1016/j.ins.2011.08.027 PDF | BiB
@article{LiKCLZS12,
author = {Ke Li and
Sam Kwong and
Jingjing Cao and
Miqing Li and
Jinhua Zheng and
Ruimin Shen},
title = {Achieving balance between proximity and diversity in multi-objective
evolutionary algorithm},
journal = {Inf. Sci.},
volume = {182},
number = {1},
pages = {220--242},
year = {2012},
url = {https://doi.org/10.1016/j.ins.2011.08.027},
doi = {10.1016/j.ins.2011.08.027},
timestamp = {Wed, 14 Nov 2018 10:27:37 +0100},
biburl = {https://dblp.org/rec/bib/journals/isci/LiKCLZS12},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Multi-Objective Differential Evolution with Self-Navigation Ke Li, Sam Kwong, Ran Wang, Jingjing Cao, Imre J. Rudas
Proc. of 2012 IEEE International Conference on Systems, Mans and Cybernetics (SMC’12), IEEE Press: p. 508–513, October 2012. 10.1109/ICSMC.2012.6377775 PDF | BiB
@inproceedings{LiKWCR12,
author = {Ke Li and
Sam Kwong and
Ran Wang and
Jingjing Cao and
Imre J. Rudas},
title = {Multi-objective differential evolution with self-navigation},
booktitle = {SMC'12: Proc. of the 2012 {IEEE} International Conference on Systems, Man,
and Cybernetics},
pages = {508--513},
year = {2012},
url = {https://doi.org/10.1109/ICSMC.2012.6377775},
doi = {10.1109/ICSMC.2012.6377775},
timestamp = {Mon, 29 Jan 2018 16:21:37 +0100},
biburl = {https://dblp.org/rec/bib/conf/smc/LiKWCR12},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Weighted Voting Method Using Minimum Square Error based on Extreme Learning Machine
Jingjing Cao, Sam Kwong, Ran Wang, Ke Li
Proc. of 2012 International Conference on Machine Learning and Cybernetics (ICMLC’12), IEEE Press: p. 411–414, July 2012. 10.1109/ICMLC.2012.6358949 PDF | BiB
@inproceedings{CaoKWL12,
author = {Jingjing Cao and
Sam Kwong and
Ran Wang and
Ke Li},
title = {A weighted voting method using minimum square error based on Extreme
Learning Machine},
booktitle = {ICMLC'12: Proc. of the 2012 International Conference on Machine Learning and Cybernetics},
pages = {411--414},
year = {2012},
url = {https://doi.org/10.1109/ICMLC.2012.6358949},
doi = {10.1109/ICMLC.2012.6358949},
timestamp = {Mon, 29 Jan 2018 16:21:37 +0100},
biburl = {https://dblp.org/rec/bib/conf/icmlc/CaoKWL12},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Combining Interpretable Fuzzy Rule-based Classifiers via Multi-Objective Hierarchical Evolutionary Algorithm
Jingjing Cao, Hanli Wang, Sam Kwong, Ke Li
Proc. of 2011 IEEE International Conference on Systems, Mans and Cybernetics (SMC’11), IEEE Press: p. 1771–1776, October 2011. 10.1109/ICSMC.2011.6083928 PDF | BiB
@inproceedings{CaoWKL11,
author = {Jingjing Cao and
Hanli Wang and
Sam Kwong and
Ke Li},
title = {Combining interpretable fuzzy rule-based classifiers via multi-objective
hierarchical evolutionary algorithm},
booktitle = {ICMLC'11: Proc. of the 2011 {IEEE} International Conference on Systems, Man
and Cybernetics},
pages = {1771--1776},
year = {2011},
url = {https://doi.org/10.1109/ICSMC.2011.6083928},
doi = {10.1109/ICSMC.2011.6083928},
timestamp = {Mon, 27 Nov 2017 16:55:26 +0100},
biburl = {https://dblp.org/rec/bib/conf/smc/CaoWKL11},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
JGBL paradigm: A Novel Strategy to Enhance the Exploration Ability of NSGA-II Ke Li, Sam Kwong, Kim-Fung Man
Proc. of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO’11), ACM Press: p. 99–100, July 2011. 10.1145/2001858.2001915 PDF | BiB
@inproceedings{LiKM11,
author = {Ke Li and
Sam Kwong and
Kim{-}Fung Man},
title = {{JGBL} paradigm: a novel strategy to enhance the exploration ability
of nsga-ii},
booktitle = {GECCO'11: Proc. of the 13th Annual Genetic and Evolutionary Computation Conference},
pages = {99--100},
year = {2011},
url = {https://doi.org/10.1145/2001858.2001915},
doi = {10.1145/2001858.2001915},
timestamp = {Tue, 06 Nov 2018 11:06:35 +0100},
biburl = {https://dblp.org/rec/bib/conf/gecco/LiKM11},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Multi-Objective Differential Evolution with Adaptive Control of Parameters and Operators Ke Li, Álvaro Fialho, Sam Kwong
Proc. of the 5th International Conference on Learning and Intelligent OptimizatioN (LION’11), Springer Verlag, LNCS, p. 473–487, January 2011. 10.1007/978-3-642-25566-3_37 PDF | BiB
@inproceedings{LiFK11,
author = {Ke Li and
{\'{A}}lvaro Fialho and
Sam Kwong},
title = {Multi-Objective Differential Evolution with Adaptive Control of Parameters
and Operators},
booktitle = {Learning and Intelligent Optimization - 5th International Conference,
{LION} 5, Rome, Italy, January 17-21, 2011. Selected Papers},
series = {Lecture Notes in Computer Science},
volume = {6683},
pages = {473--487},
publisher = {Springer},
year = {2011},
url = {https://doi.org/10.1007/978-3-642-25566-3\_37},
doi = {10.1007/978-3-642-25566-3\_37},
timestamp = {Tue, 14 May 2019 10:00:51 +0200},
biburl = {https://dblp.org/rec/conf/lion/LiFK11.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Novel Slicing Based Algorithm to Calculate Hypervolume for Multi-Objective Optimization Problems Ke Li, Jinhua Zheng, Miqing Li, Cong Zhou, Hui Lv
ICIC Express Letters: An International Journal of Research and Surveys, 4(4): 1113–1120, 2010. PDF | BiB
@article{LiZLZL10,
author = {Ke Li and
Jinhua Zheng and
Miqing Li and
Cong Zhou and
Hui Lv},
title = {A Novel Slicing Based Algorithm to Calculate Hypervolume for Multi-Objective Optimization Problems},
journal = {ICIC Express Letters: An International Journal of Research and Surveys},
volume = {4},
number = {4},
year = {2010},
pages = {1133-1120}
}
Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization
Miqing Li, Jinhua Zheng, Ke Li, Qizhao Yuan, Ruimin Shen
Proc. of the 11th International Conference on Parallel Problem Solving from Nature (PPSN’10), Springer Verlag, LNCS, volume 6238: p. 647–656, September 2010. 10.1007/978-3-642-15844-5_65 PDF | BiB
@inproceedings{LiZLYS10,
author = {Miqing Li and
Jinhua Zheng and
Ke Li and
Qizhao Yuan and
Ruimin Shen},
title = {Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective
Optimization},
booktitle = {PPSN'10: Proc. of 11th International Conference on Parallel Problem Solving from Nature},
pages = {647--656},
year = {2010},
url = {https://doi.org/10.1007/978-3-642-15844-5\_65},
doi = {10.1007/978-3-642-15844-5\_65},
timestamp = {Wed, 14 Nov 2018 10:55:35 +0100},
biburl = {https://dblp.org/rec/bib/conf/ppsn/LiZLYS10},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Grid-based Fitness Strategy for Evolutionary Many-Objective Optimization
Miqing Li, Jinhua Zheng, Ruimin Shen, Ke Li, Qizhao Yuan
Proc. of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO’10), ACM Press: p. 463–470, July 2010. 10.1145/1830483.1830570 PDF | BiB
@inproceedings{LiZSLY10,
author = {Miqing Li and
Jinhua Zheng and
Ruimin Shen and
Ke Li and
Qizhao Yuan},
title = {A grid-based fitness strategy for evolutionary many-objective optimization},
booktitle = {Genetic and Evolutionary Computation Conference, {GECCO} 2010, Proceedings,
Portland, Oregon, USA, July 7-11, 2010},
pages = {463--470},
publisher = {{ACM}},
year = {2010},
url = {https://doi.org/10.1145/1830483.1830570},
doi = {10.1145/1830483.1830570},
timestamp = {Tue, 06 Nov 2018 11:06:40 +0100},
biburl = {https://dblp.org/rec/conf/gecco/LiZSLY10.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
A Novel Algorithm for Non-dominated Hypervolume-based Multiobjective Optimization Ke Li, Jinhua Zheng, Miqing Li, Cong Zhou, Hui Lv
Proc. of 2009 IEEE International Conference on Systems, Mans and Cybernetics (SMC’09), IEEE Press: p. 5220–5226, December 2009. 10.1109/ICSMC.2009.5345983 PDF | BiB
@inproceedings{LiZLZL09,
author = {Ke Li and
Jinhua Zheng and
Miqing Li and
Cong Zhou and
Hui Lv},
title = {A Novel Algorithm for Non-dominated Hypervolume-based Multiobjective
Optimization},
booktitle = {SMC'09: Proc. of the 2009 {IEEE} International Conference on Systems, Man
and Cybernetics},
pages = {5220--5226},
year = {2009},
url = {https://doi.org/10.1109/ICSMC.2009.5345983},
doi = {10.1109/ICSMC.2009.5345983},
timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
biburl = {https://dblp.org/rec/bib/conf/smc/LiZLZL09},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An Spanning Tree Based Method For Pruning Non-Dominated Solutions in Multi- Objective Optimization Problems
Miqing Li, Jinhua Zheng, Ke Li, Jun Wu, Guixia Xiao
Proc. of 2009 IEEE International Conference on Systems, Mans and Cybernetics (SMC’09), IEEE Press: p. 4882–4887, December 2009. 10.1109/ICSMC.2009.5346322 PDF | BiB
@inproceedings{LiZLWX09,
author = {Miqing Li and
Jinhua Zheng and
Ke Li and
Jun Wu and
Guixia Xiao},
title = {An Spanning Tree Based Method For Pruning Non-Dominated Solutions
in Multi-Objective Optimization Problems},
booktitle = {SMC'09: Proc. of the 2009 {IEEE} International Conference on Systems, Man
and Cybernetics},
pages = {4882--4887},
year = {2009},
url = {https://doi.org/10.1109/ICSMC.2009.5346322},
doi = {10.1109/ICSMC.2009.5346322},
timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
biburl = {https://dblp.org/rec/bib/conf/smc/LiZLWX09},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Objective Reduction based on the Least Square Method for Large-dimensional Multiobjective Optimization Problem
Cong Zhou, Jinhua Zheng, Ke Li, Hui Lv
Proc. of the 5th International Conference on Natural Computation (ICNC’09), IEEE Press: p. 350–354, August 2009. 10.1109/ICNC.2009.40 PDF | BiB
@inproceedings{ZhouZLL09,
author = {Cong Zhou and
Jinhua Zheng and
Ke Li and
Hui Lv},
title = {Objective Reduction Based on the Least Square Method for Large-Dimensional
Multi-objective Optimization Problem},
booktitle = {ICNC'09: Proc. of the 5th International Conference on Natural Computation},
pages = {350--354},
year = {2009},
url = {https://doi.org/10.1109/ICNC.2009.40},
doi = {10.1109/ICNC.2009.40},
timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
biburl = {https://dblp.org/rec/bib/conf/icnc/ZhouZLL09},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
The Convergence Analysis of Genetic Algorithm based on Space Mating
Hui Lv, Jinhua Zheng, Jun Wu, Cong Zhou, Ke Li
Proc. of the 5th International Conference on Natural Computation (ICNC’09), IEEE Press: p. 557–562, August 2009. 10.1109/ICNC.2009.39 PDF | BiB
@inproceedings{LvZWZL09,
author = {Hui Lv and
Jinhua Zheng and
Jun Wu and
Cong Zhou and
Ke Li},
title = {The Convergence Analysis of Genetic Algorithm Based on Space Mating},
booktitle = {ICNC'09: Proc. of 5th International Conference on Natural Computation},
pages = {557--562},
year = {2009},
url = {https://doi.org/10.1109/ICNC.2009.39},
doi = {10.1109/ICNC.2009.39},
timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
biburl = {https://dblp.org/rec/bib/conf/icnc/LvZWZL09},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An Improved Multi-objective Evolutionary Algorithm based on Differential Evolution Ke Li, Jinhua Zheng
Proc. of 2009 WRI World Congress on Computer Science and Information Engineering (CSIE’09), IEEE Press: p. 825–830, March 2009. 10.1109/CSIE.2009.181 PDF | BiB
@inproceedings{LiZZL09,
author = {Ke Li and
Jinhua Zheng and
Cong Zhou and
Hui Lv},
title = {An Improved Differential Evolution for Multi-objective Optimization},
booktitle = {CSIE'09: Proc. of the 2009 {WRI} World Congress on Computer Science and Information Engineering},
pages = {825--830},
year = {2009},
url = {https://doi.org/10.1109/CSIE.2009.181},
doi = {10.1109/CSIE.2009.181},
timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
biburl = {https://dblp.org/rec/bib/conf/csie/LiZZL09},
bibsource = {dblp computer science bibliography, https://dblp.org}
}