期刊
SWARM AND EVOLUTIONARY COMPUTATION
卷 69, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.swevo.2021.100990
关键词
Particle swarm optimization; Particle swarm optimization variants; Population topology; Information propagation
资金
- National Natural Science Founda-tion Program of China [61876166, 61663046]
- Open Foundation of Key Laboratory of Software Engineering of Yunnan Province [2020SE308, 2020SE309]
- Scientific Research Fund Project of Yunnan Education Department [2019Y0007]
This study investigates the relationship between information propagation speed and algorithm performance in particle swarm optimization, finding a strong negative correlation with population diversity in early iterations. It also highlights that the impact of population topology on optimization results is similar when solving problems with the same property.
Particle swarm optimization is one of the most effective optimization algorithms motivated by bird flocking behaviours. Population topology is a key aspect of particle swarm optimization research. However, after more than twenty years of research, the effects of the population topology are still poorly understood. Previous research has established that the information propagation speed determined by the population topology has an important impact on the algorithm performance; however, the impact of information propagation speed on particle swarm optimization and its variants has not yet been investigated. In this paper, information propagation in particle swarms is described and, hence, a method of simulating information propagation in particle swarms is introduced, which is used to obtain the information propagation speed. The correlation between the information propagation speed and algorithm performance is clarified through numerical simulation. The results show that the information propagation speed has a strong negative correlation with the population diversity of particle swarm optimization and its variants in the early iterations, regardless of the adopted test function and population diversity measure. The results also show that when optimizing problems with the same property, the impact of population topology on the optimization results of particle swarm optimization and variant algorithms is similar. Further more, this study provides some guidance on the population topology selection for particle swarm optimization and its variants. These findings contribute to our understanding of the impact of population topology on particle swarm optimization and its variants, and provide a basis for population topology selection for particle swarm optimization and its variants.
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