A multi-start central force optimization for global optimization

被引：15

作者：

Liu, Yong ^{[1
,2
]}

Tian, Peng ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200052, Peoples R China

[2] Yancheng Inst Technol, Dept Fundamental Sci, Yancheng 224051, Jiangsu, Peoples R China

来源：

APPLIED SOFT COMPUTING | 2015年 / 27卷

关键词：

Central force optimization; Deterministic algorithm; Multi-start strategy; Global optimization; PARTICLE SWARM OPTIMIZATION; TRAVELING SALESMAN PROBLEM; ANT COLONY OPTIMIZATION; PERFORMANCE; ALGORITHM;

D O I：

10.1016/j.asoc.2014.10.031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：92 / 98

页数：7

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