J4 ›› 2007, Vol. 29 ›› Issue (6): 51-54.
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夏桂梅 曾建潮
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摘要:
以保证全局收敛的随机微粒群算法SPSO为基础,本文提出了一种改进的随机微粒群算法——GAR-SPSO。该方法是在SPSO的进化过程中,以轮盘赌选择机制下的遗传算法所产生的 最优个体来代替SPSO中停止的微粒,参与下一代的群体进化。通过对五个多峰的测试函数进行仿真明:在搜索空间维数相同的情况下,GAR-SPSO收敛率及收敛速度均大大优于SPPSO。
关键词: 随机微粒群算法 遗传算法 轮盘赌选择 全局优化
Abstract:
Based on the stochastic particle swarm optimization algorithm that guarantees global convergence, an improved stochastic particle swarm optimization algorithm named GAR-SPSO is proposed. During the evolution of SPSO, the optimal particles produced by the genetic algorithm of roulette wheel selection s ubstitute for the stopping particles, and take part in the evolution of the next generation. Through the experiments of five multi-modal test functions,the result of simulation proves that the speed of convergence and the rate of convergence for GAR-SPSO are better than SPSO in case of the same dimension of the search space.
Key words: stochastic particle swarm optimization, genetic algorithm, roulette wheel selection, global optimization
夏桂梅 曾建潮. 一种基于轮盘赌选择遗传算法的随机微粒群算法[J]. J4, 2007, 29(6): 51-54.
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http://joces.nudt.edu.cn/CN/Y2007/V29/I6/51