"""
Differential Evolution (DE)
This module implements Differential Evolution for single-objective optimization problems.
References
----------
[1] Storn, Rainer, and Kenneth Price. "Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces." Journal of global optimization 11.4 (1997): 341-359.
Notes
-----
Author: Jiangtao Shen
Email: j.shen5@exeter.ac.uk
Date: 2025.10.24
Version: 1.0
"""
import time
import numpy as np
from tqdm import tqdm
from ddmtolab.Methods.Algo_Methods.algo_utils import *
[docs]
class DE:
"""
Differential Evolution algorithm for single-objective optimization.
Attributes
----------
algorithm_information : dict
Dictionary containing algorithm capabilities and requirements
"""
algorithm_information = {
'n_tasks': '[1, K]',
'dims': 'unequal',
'objs': 'equal',
'n_objs': '1',
'cons': 'unequal',
'n_cons': '[0, C]',
'expensive': 'False',
'knowledge_transfer': 'False',
'n': 'unequal',
'max_nfes': 'unequal'
}
@classmethod
def get_algorithm_information(cls, print_info=True):
return get_algorithm_information(cls, print_info)
[docs]
def __init__(self, problem, n=None, max_nfes=None, F=0.5, CR=0.9, save_data=True, save_path='./Data',
name='DE', disable_tqdm=True):
"""
Initialize Differential Evolution algorithm.
Parameters
----------
problem : MTOP
Multi-task optimization problem instance
n : int or List[int], optional
Population size per task (default: 100)
max_nfes : int or List[int], optional
Maximum number of function evaluations per task (default: 10000)
F : float, optional
Scaling factor for mutation (default: 0.5)
CR : float, optional
Crossover probability (default: 0.9)
save_data : bool, optional
Whether to save optimization data (default: True)
save_path : str, optional
Path to save results (default: './Data')
name : str, optional
Name for the experiment (default: 'DE')
disable_tqdm : bool, optional
Whether to disable progress bar (default: True)
"""
self.problem = problem
self.n = n if n is not None else 100
self.max_nfes = max_nfes if max_nfes is not None else 10000
self.F = F
self.CR = CR
self.save_data = save_data
self.save_path = save_path
self.name = name
self.disable_tqdm = disable_tqdm
[docs]
def optimize(self):
"""
Execute the Differential Evolution algorithm.
Returns
-------
Results
Optimization results containing decision variables, objectives, and runtime
"""
start_time = time.time()
problem = self.problem
nt = problem.n_tasks
n_per_task = par_list(self.n, nt)
max_nfes_per_task = par_list(self.max_nfes, nt)
# Initialize population in [0,1] space and evaluate for each task
decs = initialization(problem, n_per_task)
objs, cons = evaluation(problem, decs)
nfes_per_task = n_per_task.copy()
all_decs, all_objs, all_cons = init_history(decs, objs, cons)
total_nfes = sum(max_nfes_per_task)
pbar = tqdm(total=total_nfes, initial=sum(n_per_task),
desc=f"{self.name}", disable=self.disable_tqdm)
while sum(nfes_per_task) < total_nfes:
# Skip tasks that have exhausted their evaluation budget
active_tasks = [i for i in range(nt) if nfes_per_task[i] < max_nfes_per_task[i]]
if not active_tasks:
break
for i in active_tasks:
# Generate trial vectors through DE mutation and crossover
off_decs = de_generation(decs[i], self.F, self.CR)
off_objs, off_cons = evaluation_single(problem, off_decs, i)
# Calculate constraint violations
off_cvs = np.sum(np.maximum(0, off_cons), axis=1)
parent_cvs = np.sum(np.maximum(0, cons[i]), axis=1)
# One-to-one selection matching MTO-Platform Selection_Tournament
# (epsilon = 0): replace the parent only if
# - both are infeasible and the offspring has strictly lower CV, or
# - both are feasible and the offspring has strictly lower objective.
replace_cv = (parent_cvs > off_cvs) & (parent_cvs > 0) & (off_cvs > 0)
equal_cv = (parent_cvs <= 0) & (off_cvs <= 0)
replace_f = objs[i].flatten() > off_objs.flatten()
improved = (equal_cv & replace_f) | replace_cv
decs[i][improved] = off_decs[improved, :]
objs[i][improved] = off_objs[improved, :]
cons[i][improved] = off_cons[improved, :]
nfes_per_task[i] += n_per_task[i]
pbar.update(n_per_task[i])
# Append current population to history
append_history(all_decs[i], decs[i], all_objs[i], objs[i], all_cons[i], cons[i])
pbar.close()
runtime = time.time() - start_time
# Save results
results = build_save_results(all_decs=all_decs, all_objs=all_objs, runtime=runtime, max_nfes=nfes_per_task,
all_cons=all_cons, bounds=problem.bounds, save_path=self.save_path,
filename=self.name, save_data=self.save_data)
return results