Constrained multi-objective evolutionary algorithm with an improved two-archive strategy

Solving constrained multi-objective optimization problems (CMOPs) obtains considerable attention in the evolutionary computation community. Various constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for the CMOPs in the last few decades. Among the CMOEA techniques, two archive strategy is an effective approach, and enhancing the performance of C-TAEA based on two archive framework is a promising direction. This paper proposes an improved two-archive-based evolutionary algorithm, referred to as C-TAEA2. In C-TAEA2, a new fitness evaluation strategy for the convergence archive (CA) is presented to achieve better convergence. Additionally, a fitness evaluation method is proposed to evaluate solutions of the diversity archive (DA) to further promote diversity. Moreover, new update strategies are designed for both CA and DA to reduce the computational cost. Based on the new fitness evaluation strategies, a new mating selection strategy is also developed. Experiments on different benchmark CMOPs demonstrate that C-TAEA2 obtained better or highly competitive performance compared to other state-of-the-art CMOEAs. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an improved two-archive-based evolutionary algorithm C-TAEA2, which achieved better performance for constrained multi-objective optimization problems by introducing new fitness evaluation, update, and mating selection strategies.