Adaptive Constraint Relaxation-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization

Junming Chen, Kai Zhang, Hui Zeng, Jin Yan, Jin Dai () and Zhidong Dai ()
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Junming Chen: School of Art and Design, Guangzhou University, Guangzhou 510006, China
Kai Zhang: Faculty of Humanities and Arts, Macau University of Science and Technology, Macau 999078, China
Hui Zeng: School of Design, Jiangnan University, Wuxi 214122, China
Jin Yan: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Jin Dai: Graduate School of International Studies, Yonsei University, Seoul 03722, Republic of Korea
Zhidong Dai: School of Art and Design, Guangzhou University, Guangzhou 510006, China

Mathematics, 2024, vol. 12, issue 19, 1-24

Abstract: The key problem to solving constrained multi-objective optimization problems (CMOPs) is how to achieve a balance between objectives and constraints. Unfortunately, most existing methods for CMOPs still cannot achieve the above balance. To this end, this paper proposes an adaptive constraint relaxation-based evolutionary algorithm (ACREA) for CMOPs. ACREA adaptively relaxes the constraints according to the iteration information of population, whose purpose is to induce infeasible solutions to transform into feasible ones and thus improve the ability to explore the unknown regions. Completely ignoring constraints can cause the population to waste significant resources searching for infeasible solutions, while excessively satisfying constraints can trap the population in local optima. Therefore, balancing constraints and objectives is a crucial approach to improving algorithm performance. By appropriately relaxing the constraints, it induces infeasible solutions to be transformed into feasible ones, thus obtaining more information from infeasible solutions. At the same time, it also establishes an archive for the storage and update of solutions. In the archive update process, a diversity-based ranking is proposed to improve the convergence speed of the algorithm. In the selection process of the mating pool, common density selection metrics are incorporated to enable the algorithm to obtain higher-quality solutions. The experimental results show that the proposed ACREA algorithm not only achieved the best Inverse Generation Distance (IGD) value in 54.6% of the 44 benchmark test problems and the best Hyper Volume (HV) value in 50% of them, but also obtained the best results in seven out of nine real-world problems. Clearly, CP-TSEA outperforms its competitors.

Keywords: adaptive relaxation; archive; mating pool; diversity-based ranking; constrained multi-objective optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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