突变快速收敛粒子群优化及其收敛性分析

Biao Cai, Zhishu Li, Die Fu, Jian Hu, Peng Ou, Qing Li
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引用次数: 8

摘要

粒子群优化算法是利用粒子群中个体的相互作用在搜索空间中寻找最优解的算法。虽然由快速位置收敛粒子群优化(fPSO)层和突变层组成的分层粒子群优化(HPSO)算法已被证明具有良好的性能,但作者尚未对fPSO的收敛条件进行分析。本文从数学上推导了fPSO的收敛性充分条件,并构造了一种类突变fPSO。实验结果表明,引入的mfPSO在基准测试中优于HPSO。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mutated Fast Convergent Particle Swarm Optimization and Convergence Analysis
The particle swarm optimization (PSO) is an algorithm to find out optimal solution in search space by interactions of individuals among particle population. Even though the hierarchical PSO (HPSO) algorithm, which is consisted of fast position convergence particle swarm optimization (fPSO) layer and mutation layer, has been shown to perform well,but the analysis of convergent condition of fPSO hasn't been given by the authors. In this paper, the convergent sufficient condition of fPSO in is derived mathematically, and a HPSO like mutated fPSO (mfPSO) is constructed too. Results of experiments show that the introduced mfPSO performed superiority to HPSO on benchmarks.
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