基于极值优化策略的改进的人工蜂群算法

计算机科学 ›› 2013, Vol. 40 ›› Issue (6): 247-251.

基于极值优化策略的改进的人工蜂群算法

葛宇,梁静,王学平

四川师范大学基础教学学院成都610068;成都工业学院网络中心成都610031;四川师范大学数学与软件科学学院成都 610068

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受四川省教育厅项目(12ZB112)资助

Improved Artificial Bee Colony Algorithms Based on Extremal Optimization Strategy

GE Yu,LIANG Jing and WANG Xue-ping

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 为提高人工蜂群算法在求解优化问题中的性能,结合极值优化策略提出一种改进的人工蜂群算法。改进算法基于极值优化策略高效率的寻优机制重新设计了原算法中跟随蜂的局部搜索方案,并具体给出了新方案的组元变异算子和最差组元判定规则。通过对优化问题中8个典型测试函数的仿真实验表明,与基本人工蜂群算法和已有的典型改进算法相比,改进算法在寻优精度和收敛速度上均有明显提高,在优化问题求解中体现出较强的寻优能力。

关键词: 人工蜂群算法,极值优化策略,搜索方案,局部搜索

Abstract: In order to enhance the performance of artificial bee colony algorithm in solving optimization problems,this paper proposed an improved artificial bee colony algorithm．The improved algorithm redesigns local search scheme of onlook bees based on evolution method of extremal optimization strategy,and implements operators of component mutations,formulates rules of worst component judgment．The simulation results of eight typical functions of optimization problems show that the proposed algorithm can attain significant improvement on accuracy and convergent speed,has a better solution capability,compared with the basic artificial bee colony algorithm and known improved algorithm．

Key words: Artificial bee colony algorithm,Extremal optimization strategy,Search scheme,Local search

葛宇,梁静,王学平. 基于极值优化策略的改进的人工蜂群算法[J]. 计算机科学, 2013, 40(6): 247-251. https://doi.org/

GE Yu,LIANG Jing and WANG Xue-ping. Improved Artificial Bee Colony Algorithms Based on Extremal Optimization Strategy[J]. Computer Science, 2013, 40(6): 247-251. https://doi.org/

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