.png){kind=icon}
返回
Particle swarm optimization with adaptive learning strategy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.105789.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Population diversity maintenance is a crucial task for preventing a particle swarm optimization (PSO) algorithm from being trapped in local optima. A learning strategy is an effective means of improving population diversity. However, for the canonical PSO algorithm, the learning strategy focuses mainly on the global best particle, which leads to a loss of diversity. To increase the population diversity and strengthen the global search ability in PSO, this paper proposes a PSO algorithm with an adaptive learning strategy (PSO-ALS). To better promote the performance of the learning strategy, the swarm is adaptively grouped into several subswarms. The particles in each subswarm are further classified into ordinary particles and the locally best particle, and two different learning strategies without an explicit velocity are devised for updating the particles to increase the population diversity. Thus, the global optimum is determined by comparing the fitness values of the updated best particles in each subswarm. The proposed algorithm is compared with state-of-the-art PSO variants. The experimental results illustrate that the performance of PSO-ALS is promising and competitive in terms of enhanced population diversity and global search ability. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Particle swarm optimization
Adaptive learning strategy
Multiswarm
Dynamic particle classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏
分享收藏分享收藏
查看更多内容