Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Mingcheng Zuo () and Yuan Xue
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Mingcheng Zuo: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
Yuan Xue: Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China

Mathematics, 2024, vol. 12, issue 6, 1-24

Abstract: Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

Keywords: constrained multi-objective optimization problems; population feasibility state; autonomy; evolutionary optimization; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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