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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
An improved central force optimization based on simplex method
LIU Jie1,2, WANG Yu-ping3
1. School of Mathematics and Statistics, Xi’dian University, Xi’an 710071, China; 2. College of Science, Xi’an University of Science and Technology, Xi’an 710054, China; 3. School of Computer Science and Technology, Xi’dian University, Xi’an 710071,China
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Abstract  

Considering that the existing central force optimization (CFO) cannot achieve an effective balance between the evolution speed and the quality of solutions, an improved central force optimization based on the simplex method (SM-CFO) was introduced. By periodical migration of the best individual obtained by the SM operator into the detector population of the CFO, the proposed algorithm can achieve cooperative search of the CFO and SM: with the help of CFO, SM can get away from local minima; and with SM, CFO can improve its local exploiting capability. Furthermore, in order to enhance the ability of CFO and SM, an improved Nelder-Mead SM was proposed. Through a detailed sensitivity analysis on the parameters of the proposed algorithm, some suggestions for the parameter setting were put forward. Numerical experiments and comparisons on six 2-40 dimensional benchmark functions indicate that the proposed algorithm avoids the stagnation and enhances the global search ability, and is superior to other existing algorithms.

Published: 01 December 2014
CLC:  TP 301  
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Cite this article:

LIU Jie, WANG Yu-ping. An improved central force optimization based on simplex method. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2014, 48(12): 2115-2122.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2014.12.003     OR     http://www.zjujournals.com/eng/Y2014/V48/I12/2115


一种基于单纯形法的改进中心引力优化算法

针对中心引力算法无法在演化速度和求解质量之间做到有效均衡,提出一种基于单纯形法的改进中心引力算法.该算法通过周期性地把单纯形算子得到的最优个体迁移到中心引力算法的探测器种群中,达到中心引力算法和单纯形法(SM)的协同搜索：单纯形法借助中心引力算法跳出局部最优点,中心引力算法依靠单纯形法提高局部搜索能力.为了强化两种算法的作用,将改进的单纯形法应用到算法设计中,对算法的参数进行灵敏度分析,为中心引力算法的参数设置提供建议.通过６个典型的2～40维测试函数对算法进行测试,数值试验结果表明:新算法有效地克服了停滞现象,增强了全局搜索能力,与对比算法相比性能更佳.

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