A Dual-Population-Based Evolutionary Algorithm for Constrained Multiobjective Optimization
Mengjun Ming, Anupam Trivedi, Rui Wang, Dipti Srinivasan, Tao Zhang · IEEE Transactions on Evolutionary Computation · 2021
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, termedPopulation1andPopulation2. In c-DPEA, a novel self-adaptive penalty function, termedsaPF, is designed to preserve competitive infeasible solutions inPopulation1. On the other hand, infeasible solutions inPopulation2are handled using a feasibility-oriented approach. To maintain an appropriate balance between convergence and diversity in c-DPEA, a new adaptive fitness function, namedbCAD, 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.