Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 25, Issue 4, Pages 739-753Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2021.3066301
Keywords
Statistics; Sociology; Convergence; Optimization; Evolutionary computation; Collaboration; Space exploration; Coevolutionary dual populations; constrained multiobjective optimization; convergence; diversity; feasibility
Funding
- National Natural Science Foundation of China [61773390, 72071205]
- Hunan Youth Elite Program [2018RS3081]
- Scientific Key Research Project of National University of Defense Technology [ZK18-02-09, ZZKY-ZX-11-04]
- China Scholarship Council
- National Research Foundation Singapore under its AI Singapore Programme [AISG-RP-2018-004]
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The study introduces a dual-population-based evolutionary algorithm, c-DPEA, for constrained multiobjective optimization problems (CMOPs), which achieves a balance between convergence and diversity through the design of novel self-adaptive penalty and fitness functions. Extensive experiments demonstrate the superiority of c-DPEA over six state-of-the-art CMOEAs on most test problems.
The main challenge in constrained multiobjective optimization problems (CMOPs) is to appropriately balance convergence, diversity and feasibility. Their imbalance can easily cause the failure of a constrained multiobjective evolutionary algorithm (CMOEA) in converging to the Pareto-optimal front with diverse feasible solutions. To address this challenge, we propose a dual-population-based evolutionary algorithm, named c-DPEA, for CMOPs. c-DPEA is a cooperative coevolutionary algorithm which maintains two collaborative and complementary populations, termed Population1 and Population2. In c-DPEA, a novel self-adaptive penalty function, termed saPF, is designed to preserve competitive infeasible solutions in Population1. On the other hand, infeasible solutions in Population2 are handled using a feasibility-oriented approach. To maintain an appropriate balance between convergence and diversity in c-DPEA, a new adaptive fitness function, named bCAD, is developed. Extensive experiments on three popular test suites comprehensively validate the design components of c-DPEA. Comparison against six state-of-the-art CMOEAs demonstrates that c-DPEA is significantly superior or comparable to the contender algorithms on most of the test problems.
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