计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 264-268.doi: 10.11896/j.issn.1002-137X.2016.12.048

• 智能优化 • 上一篇    下一篇

基于维度分区的果蝇优化新算法

王友卫,凤丽洲,朱建明   

  1. 中央财经大学信息学院 北京100081,吉林大学计算机科学与技术学院 长春130012,中央财经大学信息学院 北京100081
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(61272398)资助

Novel Fruit Fly Optimization Algorithm Based on Dimension Partition

WANG You-wei, FENG Li-zhou and ZHU Jian-ming   

  • Online:2018-12-01 Published:2018-12-01

摘要: 为提高果蝇算法的收敛稳定性,提出了一种基于维度分区的果蝇优化新算法。将果蝇种群均分为两组:跟随果蝇和搜索果蝇。跟随果蝇在全局最优果蝇附近实现精细化局部搜索,而搜索果蝇则将位置向量的每个维度搜索范围划分为若干个区间,通过比较各个区间的最优位置来更新果蝇位置。为加快算法收敛速度,若某搜索果蝇在连续若干次迭代过程中 均 表现最差,则在当前最优果蝇位置附近产生该果蝇的新位置。针对8种典型函数的仿真实验表明:与传统算法相比, 所提算法所需参数较少,收敛稳定性高,并且在收敛精度及收敛速度等方面具有明显优势。

关键词: 果蝇算法,收敛稳定性,维度分区,全局最优果蝇,收敛精度

Abstract: In order to improve the convergence stability of fruit fly algorithm,a novel dimension partition based fruit fly optimization algorithm was proposed.The fruit fly population is divided into two groups:the following fruit flies and the searching fruit flies.A following fruit fly realizes the accurate local searching near the global best fruit fly,and a sear-ching fruit fly divides each searching dimension of the position vector into several partitions and updates its position by comparing the performances of all partitions.In order to improve the convergence speed,if a searching fruit fly performs worst during several iterations,its new position will be generated near the global best fruit fly.The experimental results of 8 typical functions show that the proposed method needs fewer parameters,and has obvious advantages on convergence stability,convergence accuracy and convergence speed when comparing with traditional methods.

Key words: Fruit fly algorithm,Convergence stability,Dimension partition,Global best fruit fly,Convergence accuracy

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