Research Repository

Composite Differential Evolution for Constrained Evolutionary Optimization

Wang, Bing-Chuan and Li, Han-Xiong and Li, Jia-Peng and Wang, Yong (2019) 'Composite Differential Evolution for Constrained Evolutionary Optimization.' IEEE Transactions on Systems Man and Cybernetics: Systems, 49 (7). pp. 1482-1495. ISSN 2168-2216

FINAL VERSION.pdf - Accepted Version

Download (2MB) | Preview


When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the ϵ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.

Item Type: Article
Uncontrolled Keywords: Composite differential evolution (DE), constrained optimization, constraint-handling technique, evolutionary algorithm (EA)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 22 Jun 2018 08:13
Last Modified: 06 Jan 2022 13:48

Actions (login required)

View Item View Item