计算机应用 ›› 2014, Vol. 34 ›› Issue (4): 1070-1073.DOI: 10.11772/j.issn.1001-9081.2014.04.1070

• 人工智能 • 上一篇    下一篇

柯西种群分布的自适应范围粒子群优化算法

逯少华,张晓伟,鲍承强,李文宝   

  1. 电子科技大学 数学科学学院,成都 611731
  • 收稿日期:2013-10-08 修回日期:2013-12-17 出版日期:2014-04-01 发布日期:2014-04-29
  • 通讯作者: 张晓伟
  • 作者简介:逯少华(1990-),女,宁夏中卫人,主要研究方向:进化计算;
    张晓伟(1979-),男,陕西宝鸡人,讲师,博士,主要研究方向:最优化方法、进化计算;
    鲍承强(1992-),男,河南信阳人,主要研究方向:进化计算;
    李文宝(1993-),女,山西朔州人,主要研究方向:进化计算。

Adaptive range particle swarm optimization with the Cauchy distributed population

LU Shaohua,ZHANG Xiaowei,BAO Chengqiang,LI Wenbao   

  1. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China
  • Received:2013-10-08 Revised:2013-12-17 Online:2014-04-01 Published:2014-04-29
  • Contact: ZHANG Xiaowei

摘要:

为了提高粒子群优化算法的求解性能,提出了一种具有柯西种群分布的自适应范围搜索的粒子群优化算法(ARPSO/C)。该算法在种群服从柯西分布的假设下,在每一次迭代中利用个体分布的中位数和尺度参数来自适应地调整种群的搜索范围,从而在局部搜索和全局搜索之间达到了一个很好的平衡。最后的数值实验结果表明:与ARPSO和PSO算法相比,该算法收敛速度得到了显著提高,并且能够有效地克服早熟现象。

Abstract:

In order to improve the performance of the Particle Swarm Optimization (PSO), an adaptive range PSO with the Cauchy distributed population named ARPSO/C was proposed. The algorithm used the median and scale parameters to adjust self-adaptively the search range in population under the suppose of the individuals obeying the Cauchy distribution, thus balanced between local search and global search. The numerical comparison results on the proposed algorithm, ARPSO and PSO show that the presented algorithm has higher convergence speed and can overcome the prematurity.

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