粒子群优化算法及其与遗传算法的比较  

Particle Swarm Optimization Algorithm and Comparison with Genetic Algorithm

作　　者：沈艳[1] 郭兵[2] 古天祥[1] 

机构地区：[1]电子科技大学机械电子工程学院,成都610054 [2]四川大学计算机学院,成都610041

出　　处：《电子科技大学学报》2005年第5期696-699,共4页Journal of University of Electronic Science and Technology of China

基　　金：电子科技大学青年基金(jx04038)资助

摘　　要：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。该文讨论粒子群优化算法的基本原理和实现步骤,分析了该算法中各参数的设置。通过一个测试函数,对粒子群优化算法与遗传算法进行了比较,结果表明粒子群优化算法在找寻最优解效率上好于遗传算法。Particle swarm optimization, rooting from simulation of swarm of bird, solves optimization problem. Firstly, discusses particle swarm optimization algorithm principle and step of implementation, and then analyzes each of parameter. Particle swarm optimization algorithm compares with genetic algorithm through the same mathematic function. The comparative result indicates that Particle swarm optimization algorithm can obtain the optimum solutions more easily than genetic algorithm and it is a good optimization method with strong competition.

关 键 词：群集智能 粒子群 遗传算法 优化 

分 类 号：O224[理学—运筹学与控制论]