粒子群优化
抓住
群体行为
计算机科学
多群优化
过程(计算)
粒子(生态学)
数学优化
人口
相似性(几何)
算法
人工智能
数学
人口学
社会学
图像(数学)
程序设计语言
操作系统
海洋学
地质学
标识
DOI:10.1109/sis.2003.1202251
摘要
The particle swarm algorithm has just enough moving parts to make it hard to understand. The formula is very simple, it is even easy to describe the working of the algorithm verbally, yet it is very difficult to grasp in one's mind how the particles oscillate around centers that are constantly changing; how they influence one another; how the various parameters affect the trajectory of the particle; how the topology of the swarm affects its performance; and so on. This paper strips away some traditional features of the particle swarm in the search for the properties that make it work. The particle swarm algorithm is modified by eliminating the velocity formula. Variations are compared. In the process some of the mysteries of the algorithm are revealed, we discover its similarity to other stochastic population-based problem solving methods, and new avenues of investigation are suggested or implied.
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