带可变随机函数和变异算子的粒子群优化算法

周晓君; 阳春华; 桂卫华; 董天雪

doi:10.3724/SP.J.1004.2014.01339

带可变随机函数和变异算子的粒子群优化算法

doi: 10.3724/SP.J.1004.2014.01339

1.
中南大学信息科学与工程学院长沙 410083;
2.
澳大利亚联邦大学科学与信息技术及工程学院巴拉瑞特 3353

基金项目:

Supported by National Natural Science Found for Distinguished Young Scholars of China (61025015), the Foundation for Innovative Research Groups of National Natural Science Foundation of China (61321003) and the China Scholarship Council

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- 文章访问数: 1911
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- 被引次数: 0
出版历程
- 收稿日期: 2011-05-30
- 修回日期: 2013-11-21
- 刊出日期: 2014-07-20

A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China;
2.
School of Science, Infor-mation Technology and Engineering, University of Ballarat, Victoria 3353, Australia

Funds:

摘要

摘要: 标准粒子群优化算法的收敛分析表明，改变随机函数、个体历史最优，群体全局最优，有助于提高该算法的性能。为此，本文提出了一种带可变随机函数和变异算子的粒子群优化算法，即通过改变速度更新方程中的随机函数分布来调节粒子在迭代过程中飞向个体历史最优和群体全局最优的比重，通过对个体历史最优和群体全局最优进行变异来增强种群的搜索能力。实验结果证实了该算法的有效性。
- 粒子群优化算法 /
- 随机函数 /
- 变异 /
- 种群密度
Abstract: The convergence analysis of the standard particle swarm optimization (PSO) has shown that the changing of random functions, personal best and group best has the potential to improve the performance of the PSO. In this paper, a novel strategy with variable random functions and polynomial mutation is introduced into the PSO, which is called particle swarm optimization algorithm with variable random functions and mutation (PSO-RM). Random functions are adjusted with the density of the population so as to manipulate the weight of cognition part and social part. Mutation is executed on both personal best particle and group best particle to explore new areas. Experiment results have demonstrated the effectiveness of the strategy.
- Particle swarm optimization /
- random functions /
- mutation /
- population density

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