期刊
MACHINE LEARNING AND KNOWLEDGE EXTRACTION
卷 1, 期 1, 页码 157-191出版社
MDPI
DOI: 10.3390/make1010010
关键词
Particle Swarm Optimization; swarm intelligence; evolutionary computation; intelligent agents; optimization; hybrid algorithms; heuristic search; approximate algorithms; robotics and autonomous systems; applications of PSO
类别
资金
- Vanderbilt University Department of EECS
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.
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