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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

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
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
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
Citation: 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

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
详细信息
  • 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

A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

More Information
  • Fund Project:

    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

  • 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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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

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
    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

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。

English Abstract

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
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
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
Citation: 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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