Adaptive control of acceleration coefficients for particle swarm optimization based on clustering analysis
Adaptive control of acceleration coefficients for particle swarm optimization based on clustering analysis
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DOI：
10.1109/cec.2007.4424893
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Zhi-hui Zhan;Jing Xiao;Jun Zhang;Wei-neng Chen
中科院分区：
文献类型：
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作者：
Zhi-hui Zhan;Jing Xiao;Jun Zhang;Wei-neng Chen
粒子群优化（PSO）中加速系数c1和c2值的设置是进化计算中最重要和最有前途的研究领域之一。PSO中的参数c1和c2分别表示“自我认知”和“社会影响”成分，它们对探索能力和收敛能力很重要。本文采用聚类分析的方法来自适应调整PSO中c1和c2的值，而不是2.0中c1和c2的固定值。通过应用K-means算法，将种群在搜索空间中的分布聚类到每一代。采用一种基于考虑包含最佳粒子和包含最差粒子的簇的相对大小的自适应系统来调整c1和c2的值。将该方法应用于多维数学函数的优化，仿真结果表明，与固定c1和c2值的方法相比，该方法具有更快的收敛速度和更好的解。
Research into setting the values of the acceleration coefficients c1 and c2 in Particle Swarm Optimization (PSO) is one of the most significant and promising areas in evolutionary computation. Parameters c1 and c2 in PSO indicate the "self-cognitive" and "social-influence" components which are important for the ability to explore and converge respectively. Instead of using fixed value of c1 and c2 with 2.0, this paper presents the use of clustering analysis to adaptively adjust the value of these two parameters in PSO. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. An adaptive system which is based on considering the relative size of the cluster containing the best particle and the one containing the worst particle is used to adjust the values of c1 and c2. The proposed method has been applied to optimize multidimensional mathematical functions, and the simulation results demonstrate that the proposed method performs with a faster convergence rate and better solutions when compared with the methods with fixed values of c1 and c2.