| Summary: | The solution of Constrained Multi-Objective Optimization(CMOP) problems aims to reasonably allocate limited search resources to satisfy constraints and optimize the objective functions. However, the increasing complexity of the problem constraints has led to significant challenges to the solution algorithm. To address this challenge, this study proposes an adaptive constrained multi-objective evolutionary algorithm based on co-evolutionary, named ACMCA. The algorithm simultaneously evolves two populations(the main population and the archive population) with complementary functions to achieve a good balance between constraint processing and objective optimization when addressing complex constraint problems. First, the main population performs a dual reproduction. In the first reproduction process, the valuable information carried by the infeasible solution is adaptively used through the dynamic fitness distribution function such that the population emphasizes the optimization of the objective function in the early stage of evolution and feasibility in the later stage. The second reproduction cooperates with the archived population to improve the convergence and maintain diversity. Subsequently, an angle-based selection scheme is proposed to update the archived population, which ensures satisfactory population diversity while maintaining the search pressure on the Pareto Front(PF). Finally, the algorithm conducts comparison experiments with five advanced Constrained Multi-Objective Evolutionary Algorithms(CMOEAs) on 33 benchmark problems. The test results demonstrate that the proposed algorithm is more advantageous than the comparison algorithms in handling various types of CMOP problems, and its efficiency is improved by an average of about 67%.
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