Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1348-1352.DOI: 10.11772/j.issn.1001-9081.2015.05.1348

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Double subgroups fruit fly optimization algorithm with characteristics of Levy flight

ZHANG Qiantu, FANG Liqing, ZHAO Yulong   

  1. Department of Artillery Engineering, Ordnance Engineering College, Shijiazhuang Hebei 050003, China
  • Received:2014-12-03 Revised:2015-01-15 Online:2015-05-10 Published:2015-05-14


张前图, 房立清, 赵玉龙   

  1. 军械工程学院 火炮工程系, 石家庄 050003
  • 通讯作者: 张前图
  • 作者简介:张前图(1991-),男,重庆人,硕士研究生,主要研究方向:武器性能检测与故障诊断; 房立清(1969-),男,河北栾城人,教授,博士,主要研究方向:武器试验、性能检测与故障诊断; 赵玉龙(1972-),男,河北石家庄人,副教授,博士,主要研究方向:装备性能检测与故障诊断.
  • 基金资助:



In order to overcome the problems of low convergence precision and easily relapsing into local optimum in Fruit fly Optimization Algorithm (FOA), by introducing the Levy flight strategy into the FOA, an improved FOA called double subgroups FOA with the characteristics of Levy flight (LFOA) was proposed. Firstly, the fruit fly group was dynamically divided into two subgroups (advanced subgroup and drawback subgroup) whose centers separately were the best individual and the worst individual in contemporary group according to its own evolutionary level. Secondly, a global search was made for drawback subgroup with the guidance of the best individual, and a finely local search was made for advanced subgroup by doing Levy flight around the best individual, so that not only both the global and local search ability balanced, but also the occasionally long distance jump of Levy flight could be used to help the fruit fly jump out of local optimum. Finally, two subgroups exchange information by updating the overall optimum and recombining the subgroups. The experiment results of 6 typical functions show that the new method has the advantages of better global searching ability, faster convergence and more precise convergence.

Key words: Fruit fly Optimization Algorithm (FOA), Levy flight, subgroup, global convergence, fitness



关键词: 果蝇优化算法, Levy飞行, 子群, 全局收敛, 适应度

CLC Number: