摘要
粒子群优化算法是一种基于群体智能理论的全局优化算法,通过群体中粒子间的合作与竞争实现对问题空间的高效搜索。针对算法后期收敛速度较慢、易陷入局部最优的缺点,提出了一种混合粒子群算法。该算法通过改变种群初始化方法,引入交叉和变异机制等措施改善基本粒子群算法的性能。数值试验结果表明,改进型粒子群算法在提高全局寻优能力和加快收敛速度等方面均有良好的表现。
Particle swarm optimization（PSO） is a global optimization algorithm based on swarm intelligence theory,and search the problem space effectively through cooperation and competition among the individuals of the population.Aiming at the shortcoming of basic PSO algorithm,that is slow convergence rate at ending and easily plunging into the local optimum,a new hybrid PSO is proposed.By changing the method of initialization and adding the crossover and mutation to the algorithm,the hybrid PSO＇s performance is significant improved.Experimental results indicate that the modified PSO has good behavior both on improving the global convergence ability and enhancing convergence rate.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第17期85-88,共4页
Computer Engineering and Applications
关键词
粒子群优化算法
交叉
变异
混合
particle swarm optimization
crossover
mutation
hybrid
