| 带可变随机函数和变异算子的粒子群优化算法 |
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| 引用本文： | 周晓君, 阳春华, 桂卫华, 董天雪. 带可变随机函数和变异算子的粒子群优化算法. 自动化学报, 2014, 40(7): 1339-1347. doi: 10.3724/SP.J.1004.2014.01339 |
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| 作者姓名： | 周晓君 阳春华 桂卫华 董天雪 |
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| 作者单位： | 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 |
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| 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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| 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。
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| 关 键 词： | 粒子群优化算法 随机函数 变异 种群密度 |
| 收稿时间： | 2011-05-30 |
| 修稿时间： | 2013-11-21 |
A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation |
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| ZHOU Xiao-Jun, YANG Chun-Hua, GUI Wei-Hua, DONG Tian-Xue. A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation. ACTA AUTOMATICA SINICA, 2014, 40(7): 1339-1347. doi: 10.3724/SP.J.1004.2014.01339 |
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| Authors: | ZHOU Xiao-Jun YANG Chun-Hua GUI Wei-Hua DONG Tian-Xue |
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| Affiliation: | 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 |
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| 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. |
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| Keywords: | Particle swarm optimization random functions mutation population density |
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