Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (10): 56-62.DOI: 10.3778/j.issn.1002-8331.1904-0330

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Fruit Fly Optimization Algorithm with Dynamic Adjustment of Search Strategy

ZHANG Shuiping, GAO Dong   

  1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • Online:2020-05-15 Published:2020-05-13



  1. 江西理工大学 信息工程学院,江西 赣州 341000


Aiming at the shortages of basic Fruit Fly Optimization Algorithm(FOA) with slow convergence speed, easy to fall into local optimum and low search precision, a new Fruit Fly Optimization Algorithm with Dynamic Adjustment of Search Strategy(FOAASS) is proposed. Firstly, chaotic map is used to enhance the uniformity and randomness of the initial distribution of fruit fly population. Secondly, the search strategies of some fruit flies are dynamically adjusted according to the population evolutionary information. Then, the search radius is randomly selected by conversion probability and dynamically adjusted. Finally, when the algorithm falls into premature, the search strategy is changed to jump out of local optimum. The experimental results show that the proposed algorithm is much better than basic FOA and its several improved algorithms in convergence precision and convergence rate.

Key words: Fruit Fly Optimization Algorithm(FOA), swarm intelligent algorithm, dynamic step size, convergence precision


针对标准果蝇优化算法(Fruit Fly Optimization Algorithm,FOA)收敛速度慢、容易陷入局部最优及寻优精度低等缺陷,提出了一种动态调整搜索策略的果蝇优化算法(Fruit Fly Optimization Algorithm with Dynamic Adjustment of Search Strategy,FOAASS)。利用混沌映射增强种群初始位置的均匀性和随机性;根据种群进化信息动态调整部分果蝇的搜索策略;通过转换概率随机选取搜索半径并对其进行动态调整;当算法陷入早熟时,改变搜索策略以跳出局部最优。仿真实验结果表明,提出的改进算法相比标准果蝇优化算法和部分改进算法,有较好的寻优精度和收敛速度。

关键词: 果蝇优化算法(FOA), 群智能算法, 动态步长, 收敛精度