An adaptive coevolutionary resource assignment algorithm for constrained multi-objective optimization
Yutao Lai, Hairu Fan, Hai‐Lin Liu, Huangxu Sheng · Swarm and Evolutionary Computation · 2026
This paper introduces ARACMO, a two-stage multi-population coevolutionary algorithm designed to solve constrained multi-objective optimization problems. In the first stage, an auxiliary population evolves by ignoring all constraints to approximate the Unconstrained Pareto Front, sharing its offspring to help other populations cross infeasible regions and reach promising search areas. In the second stage, specialized auxiliary populations are assigned to individual sub-constraints. To manage these populations effectively, we introduce a Constraints’ Weight Assignment (CWA) mechanism, which adaptively allocates coevolutionary resources based on the difficulty and importance of each constraint. This ensures that the main population receives high-intensity cooperation from the most critical auxiliary populations. Furthermore, a Constraints Combined Mechanism (CCM) is proposed to merge similar constraints, thereby saving computational budget and providing progressively deeper auxiliary information. Experimental results across four benchmark suites demonstrate that ARACMO exhibits superior competitiveness and robustness compared to five state-of-the-art algorithms. The source code is publicly available at: https://github.com/tg980515/ARACMO .