2021/03/10 by Xinyu Shan, Ke Li, Shan, Xinyu +1 · 1 citation
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Topology Optimization in Engineering #cs.NE
paper · pdf · doi:10.48550/arxiv.2103.06382
arxiv created 2021/03/10 · openalex publication_date 2021/03/10 · arxiv updated 2021/03/12 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28
Constrained multi-objective optimization problems (CMOPs) are ubiquitous in real-world engineering optimization scenarios. A key issue in constrained multi-objective optimization is to strike a balance among convergence, diversity and feasibility. A recently proposed two-archive evolutionary algorithm for constrained multi-objective optimization (C-TAEA) has be shown as a latest algorithm. However, due to its simple implementation of the collaboration mechanism between its two co-evolving archives, C-TAEA is struggling when solving problems whose pseudo Pareto-optimal front, which does not take constraints into consideration, dominates the feasible Pareto-optimal front. In this paper, we propose an improved version C-TAEA, dubbed C-TAEA-II, featuring an improved update mechanism of two co-evolving archives and an adaptive mating selection mechanism to promote a better collaboration between co-evolving archives. Empirical results demonstrate the competitiveness of the proposed C-TAEA-II in comparison with five representative constrained evolutionary multi-objective optimization algorithms.