Coevolutionary Algorithm with Bayes Theorem for Constrained Multiobjective Optimization

Shaoyu Zhao, Heming Jia (), Yongchao Li and Qian Shi
Additional contact information
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 7, 1-23

Abstract: The effective resolution of constrained multi-objective optimization problems (CMOPs) requires a delicate balance between maximizing objectives and satisfying constraints. Previous studies have demonstrated that multi-swarm optimization models exhibit robust performance in CMOPs; however, their high computational resource demands can hinder convergence efficiency. This article proposes an environment selection model based on Bayes’ theorem, leveraging the advantages of dual populations. The model constructs prior knowledge using objective function values and constraint violation values, and then, it integrates this information to enhance selection processes. By dynamically adjusting the selection of the auxiliary population based on prior knowledge, the algorithm significantly improves its adaptability to various CMOPs. Additionally, a population size adjustment strategy is introduced to mitigate the computational burden of dual populations. By utilizing past prior knowledge to estimate the probability of function value changes, offspring allocation is dynamically adjusted, optimizing resource utilization. This adaptive adjustment prevents unnecessary computational waste during evolution, thereby enhancing both convergence and diversity. To validate the effectiveness of the proposed algorithm, comparative experiments were performed against seven constrained multi-objective optimization algorithms (CMOEAs) across three benchmark test sets and 12 real-world problems. The results show that the proposed algorithm outperforms the others in both convergence and diversity.

Keywords: multi-population optimization models; constrained multi-objective optimization; Bayes theorem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/7/1191/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/7/1191/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:7:p:1191-:d:1628067

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().