IEEE CIS Task Force 12 #

Objectives #

As the name suggests, the basic idea of the decomposition-based technique is to transform the original complex problem into simplified subproblem(s) so as to facilitate the optimization. Decomposition-based techniques have been widely used for solving both single- and multi-objective optimization problems. More specifically, in single-objective optimization, especially for the large-scale scenarios, which consider a tremendous amount of decision variables, the decomposition-based technique contains three aspects: 1) analyzing and understanding the fitness landscape and modularity structure of the underlying problem; 2) decomposing the original complex problem into several loosely coupled or independent subproblems based on the learnt characteristics; 3) using a meta-heuristic to solve these subproblems in a sequential or concurrent manner. As for multi-objective optimization, the decomposition means to decompose the original multi-objective optimization problem into a number of single-objective optimization sub-problems (or simple multi-objective optimization problems) and then uses a meta-heuristic to optimize these sub-problems simultaneously and collaboratively. In this big data era, the decomposition-based techniques used for both single- and multi-objective optimization can be sythesized to address the challenges posed by the curse of dimensionality, i.e., many objectives and large scale variables.

The key objective of this task force it to generalize the decomposition-based idea and to promote its related research, including its development, education and understanding of its sub topic areas.

The main objectives of the task force can be summarized as follows:

• create an active and healthy community to promote theme areas of decomposition-based techniques
• make student, researchers, end-users, developers, and consultants aware of the state-of-the-art
• promote the use of decomposition-based methodologies/techniques and tools
• organize conferences/workshop with IEEE CIS Technical Co-Sponsorship
• organize tutorials, workshops and special sessions
• launch edited volumes, books, and special issues in journals

Anticipated Interests #

This task force will focus on all aspects, including theory, practice and applications, of the decomposition-based technique in evolutionary computation for solving both single-, multi- and many-objective optimization problems. Topics of interest including but are not limited to the following:

• Design of novel weight vector generation methods
• Development of new decomposition methods
• Design of novel computational resource allocation strategies
• Integration of new reproduction operators
• Investigation of novel mating selection and replacement procedures
• Understanding of the relationship between subproblems and solutions
• Development of novel decomposition-based MOEAs
• Hybridization of dominance- and decomposition-based approaches
• Incorporation of user-preferences in decomposition-based MOEAs
• Extension to many-objective optimization problems
• Extension to constrained multi- and many-objective optimization problems
• Design of novel methods to analyze and understand the modularity structure
• Design of novel cooperative coevolution for large-scale optimization problems
• Theoretical analysis of the decomposition-based methods

On-Going Activities #

• Special session on Advances in Decomposition-based Evolutionary Multi-­objective Optimization (ADEMO), at the 2024 IEEE Wolrd Congress on Computational Intelligence (IEEE WCCI 2024), organized by Saúl Zapotecas-Martínez, Bilel Derbel, Ke Li and Qingfu Zhang.

Past Activities #

• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 2023 IEEE Congress on Evolutionary Computation (IEEE CEC 2023), organized by Ke Li.
• Special session on Advances in Decomposition-based Evolutionary Multi-­objective Optimization(ADEMO), at the 2023 IEEE Congress on Evolutionary Computation (IEEE CEC 2023).
• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 2023 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022), organized by Ke Li.
• Special session on Advances in Decomposition-based Evolutionary Multi-­objective Optimization (ADEMO), at the 2022 IEEE Wolrd Congress on Computatinoal Intelligence (IEEE WCCI 2022).
• Special session on Advances in Decomposition-based Evolutionary Multi-­objective Optimization, at the 2021 IEEE Congress on Computational Intelligence (IEEE CEC 2021), organized by Saúl Zapotecas-Martínez, Bilel Derbel, Ke Li and Qingfu Zhang.
• Workshop on Decomposition Techniques in Evolutionary Optimization (DTEO), at the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021). The deadline is February 4, 2021 Please consider submitting your best work to this workshop and see you in Cancun!
• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 2021 Genetic and Evolutionary Computation Conference (GECCO 2021), organised by Ke Li and Qingfu Zhang.
• Special session on Advances in Decomposition-­based Evolutionary Multi-­objective Optimization, at the 2020 IEEE Congress on Computational Intelligence (IEEE WCCI 2020), organized by Saúl Zapotecas-Martínez, Bilel Derbel, Ke Li and Qingfu Zhang.
• Workshop on Decomposition Techniques in Evolutionary Optimization (DTEO), at the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020). The deadline is March 27, 2020 Please consider submitting your best work to this workshop and see you in Cancun!
• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), organised by Ke Li and Qingfu Zhang.
• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), organised by Ke Li and Qingfu Zhang.
• Special session on Advances in Decomposition-­based Evolutionary Multi-­objective Optimization, at the 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019), organized by Saúl Zapotecas-Martínez, Bilel Derbel, Ke Li and Qingfu Zhang.
• Workshop on Decomposition Techniques in Evolutionary Optimization (DTEO), at the 2019 Genetic and Evolutionary Computation Conference (GECCO 2019). The deadline is March 27, 2019. Please consider submitting your best work to this workshop and see you in Prague!
• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 2019 Genetic and Evolutionary Computation Conference (GECCO 2019), organised by Ke Li and Qingfu Zhang.
• Workshop on Decomposition Techniques in Evolutionary Optimization (DTEO), at the 2018 Genetic and Evolutionary Computation Conference (GECCO 2018). The deadline is March 27, 2018. Please consider submitting your best work to this workshop and see you in Kyoto!
• Tutorial on Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities, at the 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), organised by Ke Li and Qingfu Zhang.
• Special issue on Recent Advances in Evolutionary Multi-Objective Optimization, at the Swarm and Evolutionary Computation journal, organized by Slim Bechikh and Carlos. A. Coello Coello. The deadline is May 30, 2017. You are highly encouraged to submit your best work here!
• Special session on Advances in Multiobjective Evolutionary Algorithms based on Decomposition, at the IEEE Congress on Evolutionary Computation (IEEE CEC 2017), organized by Anupam Trivedi, Dipti Srinivasan and Qingfu Zhang.
• Tutorial on Recent Advances in Multi-objective and Many-objective Evolutionary Algorithms, at the IEEE Congress on Evolutionary Computation (IEEE CEC 2017), organized by Anupam Trivedi and Dipti Srinivasan.
• Plenary Talk on Use of Traditional Optimization Methods in Multiobjective Evolutionary Computation, at the IEEE Congress on Evolutionary Computation (IEEE CEC 2017), delivered by Qingfu Zhang.
• Tutorial on Advances in Multi-objective Evolutionary Algorithms based on Decomposition, at the Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017), organized by Anupam Trivedi and Dipti Srinivasan.
• Special session on Advances in Decomposition­based Evolutionary Multi­objective Optimization, at the IEEE World Congress on Computational Intelligence (IEEE WCCI 2016), organized by Sa’ul Zapotecas Mart’inez, Bilel Derbel, Qingfu Zhang and Carlos A. Coello Coello.
• Tutorial on Decomposition and Cooperative Coevolution Techniques for Large Scale Global Optimization, at the IEEE Congress on Evolutionary Computation (IEEE CEC 2015), organized by Xiaodong Li.
• Tutorial on Decomposition and Cooperative Coevolution Techniques for Large Scale Global Optimization, Genetic and Evolutionary Computation Conference (GECCO 2014), organized by Xiaodong Li.

Resources #

• Repository of the state-of-the-art developments of multi-objective evolutionary algorithm based on decomposition (MOEA/D) can be found from here.

• Recent survey paper on the developments of MOEA/D:

  • A. Trivedi, D. Srinivasan, K. Sanyal, A. Ghosh, A Survey of Multiobjective Evolutionary Algorithms based on Decomposition, IEEE Trans. on Evolutionary Computation, 21(3): 440-462, 2016.
  • A. Santiago, H. Huacuja, B. Dorronsoro, J. Pecero, C. Santillan, J. Barbosa, J. Monterrubio, A Survey of Decomposition Methods for Multi-objective Optimization, Recent Advances on Hybrid Approaches for Designing Intelligent Systems, 453-465, 2014.

Chairs #

• Bilel Derbel (Chair), University of Lille, France.
• Ke Li (Vice Chair, Founding Chair), Department of Computer Science, University of Exeter, Exeter, UK.
• Qingfu Zhang (Vice Chair), Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China.

Members #

• Slim Bechikh, University of Tunis, Tunisia.
• Ran Cheng, Southern University of Science and Technology, China.
• Kalyanmoy Deb, Michigan State University, USA.
• Hisao Ishibuchi, Osaka Prefecture University, Japan.
• Yaochu Jin, University of Surrey, UK.
• Sam Kwong, City University of Hong Kong, Hong Kong SAR, China.
• Xiaodong Li, RMIT University, Australia.
• Arnaud Liefooghe, University of Lille, France.
• Hui Li, Xi’an Jiaotong University, China.
• Miqing Li, University of Birmingham, UK.
• Sanaz Mostaghim, Otto von Guericke University of Magdeburg, Germany.
• Tapabrata Ray, University of New South Wales, Australia.
• Dipti Srinivasan, National University of Singapore, Singapore.
• Hiroyuki Sato, University of Electro-communications, Japan.
• Kay Chen Tan, City University of Hong Kong, Hong Kong SAR, China.
• Ke Tang, Southern University of Science and Technology, China.
• Shengxiang Yang, De Montfort University, UK.
• Aimin Zhou, East China Normal University, China.
• Saúl Zapotecas, Unidad Cuajimalpa, México.