The main challenge in constrained multiobjective optimization problems (CMOPs) is to appropriately balance convergence, diversity and feasibility. Their imbalance can easily cause the failure of a constrained multiobjective evolutionary algorithm (CMOEA) in converging to the Pareto-optimal front with diverse feasible solutions. To address this challenge, we propose a dual-population-based evolutionary algorithm, named c-DPEA, for CMOPs. c-DPEA is a cooperative coevolutionary algorithm which maintains two collaborative and complementary populations, termed Population1 and Population2 . In c-DPEA, a novel self-adaptive penalty function, termed saPF , is designed to preserve competitive infeasible solutions in Population1 . On the other hand, infeasible solutions in Population2 are handled using a feasibility-oriented approach. To maintain an appropriate balance between convergence and diversity in c-DPEA, a new adaptive fitness function, named bCAD , is developed. Extensive experiments on three popular test suites comprehensively validate the design components of c-DPEA. Comparison against six state-of-the-art CMOEAs demonstrates that c-DPEA is significantly superior or comparable to the contender algorithms on most of the test problems.

中文翻译：

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一种用于约束多目标优化的基于双种群的进化算法

约束多目标优化问题 (CMOP) 的主要挑战是适当平衡收敛性、多样性和可行性。它们的不平衡很容易导致约束多目标进化算法 (CMOEA) 在收敛到具有不同可行解的帕累托最优前沿时失败。为了应对这一挑战，我们为 CMOP 提出了一种基于双种群的进化算法，名为 c-DPEA。c-DPEA 是一种协作协同进化算法，它维护两个协作和互补的种群，称为人口 1 和 人口2。在 c-DPEA 中，一种新颖的自适应惩罚函数，称为saPF ，旨在保留具有竞争力的不可行解决方案 人口1。另一方面，不可行的解决方案使用面向可行性的方法处理人口 2。为了在 c-DPEA 中保持收敛性和多样性之间的适当平衡，一种新的自适应适应度函数，命名为bCAD，开发。在三个流行的测试套件上进行的大量实验全面验证了 c-DPEA 的设计组件。与六个最先进的 CMOEA 的比较表明，在大多数测试问题上，c-DPEA 明显优于或可与竞争算法相媲美。