A Two‐Archive Constrained Multi‐Objective Optimization Algorithm Based on Two‐Stage Weak Cooperation

ABSTRACT Constrained multi‐objective optimization problems widely exist in real‐world applications, yet remain challenging due to the coexistence of multiple conflicting objectives and constraints. This study proposes a two‐archive constrained multi‐objective optimization algorithm with two‐stage weak cooperation, named BWC‐TAA. First, based on the two‐archive framework, a weak cooperation interaction mechanism is designed, in which the CA and DA evolve independently. They only merge offspring during the update stage to select high‐quality individuals, thereby preventing solutions from being confined to the parent population range. Second, a two‐stage evolutionary strategy is introduced to dynamically adjust the optimization objectives, enabling CA and DA to adopt different strategies in different phases. Finally, BWC‐TAA is evaluated on benchmark constrained problems against eight state‐of‐the‐art algorithms. The experimental results demonstrate that BWC‐TAA significantly outperforms the compared algorithms in terms of convergence and diversity indicators.