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Continuous updating and maintenance of feasible solutions is crucial when solving constrained multi-objective optimization problems (CMOPs). However, most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not efficient enough in updating and preserving competitive feasible solutions, thus reducing population diversity. To address this issue, this paper proposes a dual-population (i.e., mainPop and auxPop) constrained multi-objective evolutionary algorithm with a feasible archive set for CMOPs, named DPFAS. The two populations have different functions in the algorithm. Specifically, the ݉ܽ݅݊ܲmainPop considers both objectives and constraints for solving the original CMOPs, while the ܽauxPop is used only for the optimization of objectives without considering constraints. In addition, a feasible archive set is used to store feasible solutions that are competitive in the ܽauxPop and provide useful information for the ݉ܽ݅݊ܲmainPop. Moreover, a fitness assignment strategy is designed to speed up the algorithm’s convergence. Particularly, the population converges faster by selecting better-nondominated solutions into the matching pool. Finally, experimental studies on 23 benchmark functions show that the proposed algorithm was more competitive compared with five state-of-the-art CMOEAs.
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