一种具有变随机函数和突变的粒子群优化算法

Q2 Computer Science

自动化学报 Pub Date : 2014-07-01 DOI:10.1016/S1874-1029(14)60015-X

Xiao-Jun ZHOU , Chun-Hua YANG , Wei-Hua GUI , Tian-Xue DONG

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引用次数: 17

摘要

对标准粒子群优化算法的收敛性分析表明，随机函数、个人最优和群体最优的变化有可能提高标准粒子群优化算法的性能。本文在粒子群优化算法中引入了一种具有变随机函数和多项式突变的新策略，即变随机函数和突变粒子群优化算法(PSO- rm)。随机函数随人口密度的变化而调整，从而操纵认知部分和社会部分的权重。对个人最佳粒子和群体最佳粒子进行变异，探索新的领域。实验结果证明了该策略的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

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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来源期刊

自动化学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

4.80

自引率

0.00%

发文量

6655

期刊介绍： ACTA AUTOMATICA SINICA is a joint publication of Chinese Association of Automation and the Institute of Automation, the Chinese Academy of Sciences. The objective is the high quality and rapid publication of the articles, with a strong focus on new trends, original theoretical and experimental research and developments, emerging technology, and industrial standards in automation.