Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

Qingqing Liu, Caixia Cui and Qinqin Fan
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Qingqing Liu: Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
Caixia Cui: Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
Qinqin Fan: Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China

Mathematics, 2022, vol. 10, issue 5, 1-23

Abstract: The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.

Keywords: constrained multi-objective optimization; evolutionary computation; reinforcement learning; SARSA method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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