A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization

Date

2022-09-22

Advisors

Journal Title

Journal ISSN

ISSN

0020-0255

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

With the popularity of “flipped classrooms,” teachers pay more attention to cultivating students’ autonomous learning ability while imparting knowledge. Inspired by this, this paper proposes a Self-exploratory Competitive Swarm Optimization algorithm for Large-scale Multiobjective Optimization (SECSO). Its idea is very simple and there are no parameters that need to be adjusted. Particles evolve by exploring their neighboring space and learning from other particles in the swarm, thereby simultaneously enhancing the diversity and convergence performance of the algorithm. Compared with eight state-of-the-art large-scale multiobjective evolutionary algorithms, the proposed method exhibited outstanding performance on LSMOP problems with up to 10,000 decision variables. Unlike most existing large-scale evolutionary algorithms that usually require a large number of objective evaluations, SECSO shows the ability to find a set of well converged and diverse non-dominated solutions.

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

Evolutionary algorithms, Large-scale optimization, Multiobjective optimization, Self-exploratory

Citation

Qi, S., Zou, J., Yang, S., Jin, Y., Zheng, J. and Yang. X. (2022) A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization. Information Sciences, 609, pp. 1601-1620

Rights

Research Institute

Institute of Artificial Intelligence (IAI)