期刊
INFORMATION SCIENCES
卷 519, 期 -, 页码 332-347出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.01.049
关键词
Multimodal optimization; Particle swarm optimization; Ring neighborhood topology; Niche
资金
- National Natural Science Foundation of China [61876164, 61673331, 61772178]
- Education Department Major Project of Hunan Province [17A212]
- Science and Technology Plan Project of Hunan Province [2018TP1036, 2016TP1020]
- Provinces and Cities Joint Foundation Project [2017J04001]
Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. But these parameters are usually difficult to set because they depend on the problem. The particle swarm optimization algorithm using the ring neighborhood topology does not require any niche parameters, but the determination of the particle neighborhood in this method is based on the subscript of the particle, and the result fails to achieve the best performance. For better performance, in this paper, a particle swarm optimization algorithm based on the ring neighborhood topology of Euclidean distance between particles is proposed, which is called the close neighbor mobility optimization algorithm. The algorithm mainly includes the following three strategies: elite selection mechanism, close neighbor mobility strategy and modified DE strategy. It mainly uses the Euclidean distance between particles. Each particle forms its own unique niche, evolves in a local scope, and finally locates multiple global optimal solutions with high precision. The algorithm greatly improves the accuracy of the particle. The experimental results show that the close neighbor mobility optimization algorithm has better performance than most single-objective multi-modal algorithms. (C) 2020 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据