Particle swarm optimization
Particle swarm optimization
复制标题
DOI：
10.4249/scholarpedia.1486
复制
发表时间：
2008-11
期刊：
影响因子：
--
通讯作者：
Marco Dorigo;M. M. D. Oca-M.;A. Engelbrecht
中科院分区：
文献类型：
--
作者：
Marco Dorigo;M. M. D. Oca-M.;A. Engelbrecht
1：示例人口拓扑。最左边的图片描绘了完全连接的拓扑，也就是说（自链不是为简单而绘制）。中心的图片描绘了所谓的冯·诺伊曼拓扑，其中。最右边的图片描绘了一个环拓扑，其中每个粒子都是其他两个粒子的邻居。粒子群优化（PSO）是一种基于人群的随机方法，用于解决连续和离散优化问题。在粒子群优化中，简单的软件代理（称为颗粒）在优化问题的搜索空间中移动。粒子的位置代表了手头优化问题的候选解决方案。每个粒子通过根据最初受鸟类植物行为模型启发的规则改变速度来搜索搜索空间中的更好位置。粒子群优化属于用于解决优化问题的群智能技术类别。
1: Example population topologies. The leftmost picture depicts a fully connected topology, that is, (self-links are not drawn for simplicity). The picture in the center depicts a so-called von Neumann topology, in which. The rightmost picture depicts a ring topology in which each particle is neighbor to two other particles. Particle swarm optimization (PSO) is a population-based stochastic approach for solving continuous and discrete optimization problems. In particle swarm optimization, simple software agents, called particles, move in the search space of an optimization problem. The position of a particle represents a candidate solution to the optimization problem at hand. Each particle searches for better positions in the search space by changing its velocity according to rules originally inspired by behavioral models of bird flocking. Particle swarm optimization belongs to the class of swarm intelligence techniques that are used to solve optimization problems.