A Constrained Multi-Objective Optimization Algorithm with a Population State Discrimination Model

Shaoyu Zhao, Heming Jia (), Yongchao Li and Qian Shi
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Shaoyu Zhao: School of Information Engineering, Sanming University, Sanming 365004, China
Heming Jia: School of Information Engineering, Sanming University, Sanming 365004, China
Yongchao Li: School of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Qian Shi: School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China

Mathematics, 2025, vol. 13, issue 5, 1-27

Abstract: The solution to constrained multi-objective optimization problems (CMOPs) requires optimizing the objective functions while satisfying the constraint conditions. To effectively address CMOPs, algorithms must balance objectives and constraints. However, the limited adaptability of specific constraint-handling techniques (CHTs) has hindered the widespread applicability of constrained multi-objective evolutionary algorithms (CMOEAs). To overcome this limitation, this article proposes a population state-based CMOEA. First, a model is developed to identify population states based on the positions of the primary and auxiliary populations. Tailored environmental selection models are then designed for the auxiliary population according to different states, enabling them to guide the evolution of the main population more effectively. By dynamizing the CHTs, the proposed algorithm can adapt to a broader and more complex range of CMOPs. Additionally, state-specific optimal individual selection methods are introduced, allowing the auxiliary population to escape local optima and accelerate exploration. A simple yet effective resource allocation model is incorporated to address the potential computational resource waste associated with dual populations, enhancing the resource utilization. Comprehensive tests, including comparisons with seven state-of-the-art algorithms, were conducted on 47 benchmark functions and 12 real-world problems. The experimental results demonstrate that the proposed CMOEA outperforms existing CMOEAs in its convergence and diversity.

Keywords: constrained multi-objective optimization; multiple population optimization models; population state discrimination (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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