粒子群优化算法研究

Research on Particle Swarm Optimization Algorithm

粒子群优化算法是根据鸟群觅食过程中的迁徙和群集模型而提出的,用于解决优化问题的一类新兴的随机优化算法。本文首先介绍PSO算法的基本原理和工作机制;然后介绍粒子群优化算法的优化策略,包括提高收敛速度﹑算法离散化﹑提高总群多样性;最后对其将来的发展进行了展望。

Particle swarm optimization algorithm is put forward according to the simulation of migration of bird flight their food-searching and the group model,and is a novel stochastic optimization algorithm which can use to solve optimization problems. The models of bird flocking and swarm actions are firstly introduced,and the fundamentals characteristics and the working mechanisms of PSO algorithm are also analyzed. Then this paper introduces the optimization strategy of particle swarm optimization including improve the convergence rate,discrete algorithms,improve overall group diversity. Finally,some suggestion on future trends and existing problems related to PSO algorithm are discussed and concluded.