A novel two-archive evolutionary algorithm for constrained multi-objective optimization with small feasible regions

Constrained multi-objective evolutionary algorithms (CMOEAs) have been extensively studied in recent years. However, the performance of most of traditional CMOEAs is unsatisfied for constrained multi-objective optimization problems (CMOPs) with small feasible regions. Based on the idea of two-archive, this paper proposes a novel two-archive evolutionary algorithm for constrained multi objective optimization with small feasible regions. Specifically, we maintain two archives, named convergence-oriented archive (CA) and diversity-oriented archive (DA). To handle the CMOPs which feasible regions are small and far from the unconstrained Pareto front (PF), a cooperation-based mating selection mechanism is proposed. To strike a balance among convergence, diversity, and feasibility, a high-quality solution selection mechanism is proposed, which can help the CA approach PF from different directions and balance the convergence and diversity. To provide better diversity, a dynamic selection strategy is designed to update DA according to the status of the CA. In addition, in order to make the population evenly distributed in feasible regions, a replacement mechanism of the ideal point is designed. Compared with the four state-of-the-art constrained multi-objective evolutionary optimization algorithms, comprehensive experiments on a series of benchmark problems fully demonstrate the superiority of the proposed algorithm in terms of increment p and hypervolume (HV). (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel two-archive evolutionary algorithm for constrained multi-objective optimization problems with small feasible regions. The algorithm achieves a balance between convergence, diversity, and feasibility through mechanisms such as cooperation-based mating selection, high-quality solution selection, dynamic selection strategy, and ideal point replacement. Comprehensive experiments demonstrate the superiority of the proposed algorithm in terms of increment p and hypervolume compared to state-of-the-art algorithms.