A flexible two-stage constrained multi-objective evolutionary algorithm based on automatic regulation

The core element in solving constrained multi-objective problems (CMOPs) with evolutionary algorithms is simultaneously balancing objective optimization and constraint satisfaction. Maintaining this balance becomes more challenging for existing when dealing complex CMOPs, as various feasible regions often result CMOPs very different characteristics. To address issue, we propose a flexible two-stage algorithm based on automatic regulation (ARCMO), which can effectively control trends to adapt CMOPs. Specifically, the first stage performs fast global search passes population information second stage. consists of two dynamically complementary sub-processes: exploration subprocess convergence subprocess. ratio these subprocesses adjusted from stage, allowing ARCMO complexities. Experiments several recently proposed benchmark suites real-world application show that adaptable than contender