A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization
A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization
复制标题
一种用于大规模多目标优化的自探索竞争群体优化算法
DOI：
10.1016/j.ins.2022.07.110
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发表时间：
2022-07
影响因子：
8.1
通讯作者：
杨旭
中科院分区：
文献类型：
--
作者：
齐晟;邹娟;杨圣祥;金耀初;郑金华;杨旭
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.
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