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<p>Constrained multi-objective optimization problems (CMOPs) are challenging for evolutionary algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts (CPFs) exhibit various complex characteristics, e.g., degeneracy, discontinuity or irregularity. Most algorithms achieve poor convergence and diversity performance on these problems. Therefore, we proposed a coevolutionary framework based on constraints decomposition to solve complex CMOPs. Specifically, this framework decomposes the CMOP into multiple help subproblems with a single constraint, thereby decoupling the complex constraints. Then, multiple subpopulations optimize these subproblems to assist in solving the original problem. In addition, a two-stage strategy is used to fully utilize the auxiliary populations to search for feasible solutions. In addition, an evolutionary state detection strategy based on historical information is proposed, which is used to determine whether the evolution moves to the next stage. The framework can take the advantage of the low complexity of single-constraint problems to help algorithm search the complete feasible regions. Experiments on benchmark problems show that the proposed algorithm is competitive with eight other most representative constrained evolutionary algorithms in terms of convergence and diversity performance.</p>

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A Coevolutionary Algorithm Based on Constraints Decomposition for Constrained Multi-objective Optimization Problems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月5日

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  • Guangpeng Li,
  • Li Li,
  • Guoyong Cai

摘要

Constrained multi-objective optimization problems (CMOPs) are challenging for evolutionary algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts (CPFs) exhibit various complex characteristics, e.g., degeneracy, discontinuity or irregularity. Most algorithms achieve poor convergence and diversity performance on these problems. Therefore, we proposed a coevolutionary framework based on constraints decomposition to solve complex CMOPs. Specifically, this framework decomposes the CMOP into multiple help subproblems with a single constraint, thereby decoupling the complex constraints. Then, multiple subpopulations optimize these subproblems to assist in solving the original problem. In addition, a two-stage strategy is used to fully utilize the auxiliary populations to search for feasible solutions. In addition, an evolutionary state detection strategy based on historical information is proposed, which is used to determine whether the evolution moves to the next stage. The framework can take the advantage of the low complexity of single-constraint problems to help algorithm search the complete feasible regions. Experiments on benchmark problems show that the proposed algorithm is competitive with eight other most representative constrained evolutionary algorithms in terms of convergence and diversity performance.