Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

Kai Zhang, Siyuan Zhao, Hui Zeng and Junming Chen ()
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Kai Zhang: Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, China
Siyuan Zhao: Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, China
Hui Zeng: School of Design, Jiangnan University, Wuxi 214122, China
Junming Chen: Faculty of Humanities and Arts, Macau University of Science and Technology, Macao 999078, China

Mathematics, 2025, vol. 13, issue 3, 1-25

Abstract: The core issue in handling constrained multi-objective optimization problems (CMOP) is how to maintain a balance between objectives and constraints. However, existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to achieve the desired performance when confronted with complex feasible regions. Building upon this theoretical foundation, a two-stage archive-based constrained multi-objective evolutionary algorithm (CMOEA-TA) based on genetic algorithms (GA) is proposed. In CMOEA-TA, First stage: The archive appropriately relaxes constraints based on the proportion of feasible solutions and constraint violations, compelling the population to explore more search space. Second stage: Sharing valuable information between the archive and the population, while embedding constraint dominance principles to enhance the feasibility of solutions. In addition an angle-based selection strategy was used to select more valuable solutions to increase the diversity of the population. To verify its effectiveness, CMOEA-TA was tested on 54 CMOPs in 4 benchmark suites and 7 state-of-the-art algorithms were compared. The experimental results show that it is far superior to seven competitors in inverse generation distance (IGD) and hypervolume (HV) metrics.

Keywords: constrained multi-objective optimization; evolutionary algorithm; two-stage; archive (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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