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

With the popularity of flipped classrooms, teachers pay more attention to cultivating stu-dents' 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 exist-ing 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.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a Self-exploratory Competitive Swarm Optimization algorithm for Large-scale Multiobjective Optimization (SECSO), which enhances algorithm diversity and convergence performance by exploring neighboring space and learning from other particles. Compared with other large-scale evolutionary algorithms, SECSO shows outstanding performance on LSMOP problems.