Dynamic Constrained Boundary Method for Constrained Multi-Objective Optimization

Qiuzhen Wang, Zhibing Liang (), Juan Zou, Xiangdong Yin, Yuan Liu, Yaru Hu and Yizhang Xia
Additional contact information
Qiuzhen Wang: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science of Xiangtan University, Xiangtan 411100, China
Zhibing Liang: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science of Xiangtan University, Xiangtan 411100, China
Juan Zou: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science of Xiangtan University, Xiangtan 411100, China
Xiangdong Yin: Faculty of Informational Engineering, Hunan University of Science and Engineering, Yongzhou 411201, China
Yuan Liu: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science of Xiangtan University, Xiangtan 411100, China
Yaru Hu: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science of Xiangtan University, Xiangtan 411100, China
Yizhang Xia: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science of Xiangtan University, Xiangtan 411100, China

Mathematics, 2022, vol. 10, issue 23, 1-16

Abstract: When solving complex constrained problems, how to efficiently utilize promising infeasible solutions is an essential issue because these promising infeasible solutions can significantly improve the diversity of algorithms. However, most existing constrained multi-objective evolutionary algorithms (CMOEAs) do not fully exploit these promising infeasible solutions. In order to solve this problem, a constrained multi-objective optimization evolutionary algorithm based on the dynamic constraint boundary method is proposed (CDCBM). The proposed algorithm continuously searches for promising infeasible solutions between UPF (the unconstrained Pareto front) and CPF (the constrained Pareto front) during the evolution process by the dynamically changing auxiliary population of the constraint boundary, which continuously provides supplementary evolutionary directions to the main population and improves the convergence and diversity of the main population. Extensive experiments on three well-known test suites and three real-world constrained multi-objective optimization problems demonstrate that CDCBM is more competitive than seven state-of-the-art CMOEAs.

Keywords: constrained multi-objective optimization problems (CMOPs); dynamic constrained boundary (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:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4459/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4459/ (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:10:y:2022:i:23:p:4459-:d:984778

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 ().