Combining state detection with knowledge transfer for constrained multi-objective optimization

Date

0202-04-18

Advisors

Journal Title

Journal ISSN

ISSN

2375-0197

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

The main challenge in studying constrained multi-objective optimization problems (CMOPs) is reasonably balancing convergence, diversity, and feasibility. One of the most successful solutions to this challenge is the co-evolutionary frame-work, an algorithm in which multiple populations cooperate and complement each other, with different but interdependent populations addressing different but related problems. However, the effectiveness of existing algorithms for information exchange between various populations is not apparent. This paper proposes a new algorithm named SDKT using population state detection and knowledge transfer. The method has dual stages (i.e., knowledge acquisition and knowledge reception) and dual populations. Specifically, by restarting the strategy, these two populations (i.e., mainPop and auxPop) first explore more feasible regions with and without constraints. Then, in the knowledge receiving stage, ma i nPop and auxPop provide effective information to promote each other's approach to constrained PF (CPF) and unconstrained PF (UPF), respectively. Extensive experiments on three well-known test suites and three real-world problem studies fully demonstrate that SDKT is more competitive than five state-of-the-art constrained multi-objective evolutionary algorithms.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Constrained multi-objective optimization problems, co-evolutionary framework, knowledge acquisition, knowledge reception

Citation

J. Zheng, K. Yang, J. Zou, and S. Yang. (2023) Combining state detection with knowledge transfer for constrained multi-objective optimization. Proceedings of the IEEE 34th International Conference on Tools with Artificial Intelligence, pp. 712-719

Rights

Research Institute

Institute of Artificial Intelligence (IAI)