A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization
A Novel Dual-Stage Dual-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization
复制标题
一种新颖的约束多目标优化双阶段双种群进化算法
DOI：
10.1109/tevc.2021.3131124
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发表时间：
2021
影响因子：
14.3
通讯作者：
Tao Zhang
中科院分区：
文献类型：
--
作者：
Mengjun Ming;Rui Wang;Hisao Ishibuchi;Tao Zhang
除了寻找可行解之外，利用信息丰富的不可行解对于解决约束多目标优化问题（CMOP）也很重要。然而，大多数现有的约束多目标进化算法（CMOEA）无法有效地探索和利用这些解决方案，因此在面对具有大的不可行区域的问题时表现出较差的性能。为了解决这个问题，本文提出了一种称为 DD-CMOEA 的新方法，其特点是双阶段（即勘探和开发）和双种群。具体来说，称为 mainPop 和 auxPop 的两个种群首先在考虑和不考虑约束的情况下单独进化，分别负责探索可行和不可行的解决方案。然后，在开发阶段，mainPop提供有关可行区域位置的信息，这有助于auxPop找到并利用周围的不可行解。 auxPop 获得的有希望的不可行解反过来又帮助 mainPop 更好地收敛到帕累托最优前沿。对三个著名测试套件的广泛实验和真实案例研究充分证明 DD-CMOEA 比五个最先进的 CMOEA 更具竞争力。
In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multiobjective optimization problems (CMOPs). However, most of the existing constrained multiobjective evolutionary algorithms (CMOEAs) cannot effectively explore and exploit those solutions and, therefore, exhibit poor performance when facing problems with large infeasible regions. To address the issue, this article proposes a novel method, called DD-CMOEA, which features dual stages (i.e., exploration and exploitation) and dual populations. Specifically, the two populations, called mainPop and auxPop, first individually evolve with and without considering the constraints, responsible for exploring feasible and infeasible solutions, respectively. Then, in the exploitation stage, mainPop provides information about the location of feasible regions, which facilitates auxPop to find and exploit surrounding infeasible solutions. The promising infeasible solutions obtained by auxPop in turn help mainPop converge better toward the Pareto-optimal front. Extensive experiments on three well-known test suites and a real-world case study fully demonstrate that DD-CMOEA is more competitive than five state-of-the-art CMOEAs.