Cooperative Differential Evolution Framework for Constrained Multiobjective Optimization

Jiahai Wang, Guanxi Liang, Jun Zhang

Abstract: This paper presents a cooperative differential evolution framework (CCMODE) for constrained multiobjective optimization, and two instantiations of the CCMODE framework are implemented. The proposed framework has (M+1) populations, including M subpopulations for constrained single-objective optimization and an archive population for constrained M-objective optimization. Each subpopulation performs its own constrained single-objective differential evolution to optimize the assigned constrained single-objective optimization problem. For the archive population, the constraint handling techniques (CHTs) are modified for constrained multiobjective optimization. The proposed framework takes the advantage of existing effective constrained single-objective optimization algorithms, and extends them to deal with constrained multiobjective optimization problems. In two instantiations, two CHTs are implemented in CCMODE framework, respectively. Experiment results on several sets of benchmark problems with two, three, and many objectives show that the proposed algorithm is better than existing state-of-the-art constrained multiobjective evolutionary algorithms. The effectiveness of the subpopulations is also discussed.