摘要
粒子群优化算法(PSO)源于对鸟群捕食系统的模拟,是近年来被广为关注和研究的一种智能优化算法。PSO算法属于进化算法的一种,比遗传算法(GA)更简单易实现,且没有交叉和变异操作,需要设定的参数也不多,收敛速度快。目前已广泛应用于函数优化、神经网络训练、模糊系统控制以及其他遗传算法等领域。目前PSO的研究主要集中在算法本身和算法的应用研究两个方面。
Particle swami algorithm（PSO）,originating from the simulation of birds flock’s looking for food,has been paid attention and researched wildly.Compared to Genetic Algorithm,PSO Can be implemented easily because it hasn’t crossover and mutation operation and less parameters to be adjusted.Meanwhile,PSO’s convergence speed is generally faster than GA.Due to these advantages above mentioned,PSO has been wildly applied to the object functions optimization,Fuzzy System Control,neural network training,and so on.Up to now,it is mainly consisted of two aspects for PSO research： One is the algorithm research,the other is the application study for PSO.
出处
《农业网络信息》
2010年第7期146-148,共3页
Agriculture Network Information
关键词
粒子群优化
改进的粒子群算法
早熟
PSO
improved particle swarm algorithm
premature convergence
