Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3113-3119.DOI: 10.11772/j.issn.1001-9081.2021010064

• Artificial intelligence • Previous Articles     Next Articles

Artificial bee colony algorithm based on multi-population combination strategy

Wenxia LI, Linzhong LIU(), Cunjie DAI, Yu LI   

  1. School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou Gansu 730070,China
  • Received:2021-01-13 Revised:2021-03-26 Accepted:2021-04-21 Online:2021-06-04 Published:2021-11-10
  • Contact: Linzhong LIU
  • About author:LI Wenxia, born in 1993, Ph. D. candidate. Her research interests include logistics supply chain management and algorithm optimization.
    DAI Cunjie, born in 1982, Ph. D., associate professor. His research interests include transportation network optimization.
    LI Yu, born in 1990, Ph. D. candidate. Her research interests include logistics supply chain management.
  • Supported by:
    the National Natural Science Foundation of China(71671079);the Gansu Natural Science Foundation(20JR5RA422);the Cooperation Project of Tianjin University-Lanzhou Jiaotong University Independent Innovation Fund(2020054)

基于多种群组合策略的人工蜂群算法

李文霞, 刘林忠(), 代存杰, 李玉   

  1. 兰州交通大学 交通运输学院,兰州 730070
  • 通讯作者: 刘林忠
  • 作者简介:李文霞(1993—),女,甘肃兰州人,博士研究生,主要研究方向:物流供应链管理及算法优化
    代存杰(1982—),男,山东郓城人,副教授,博士,主要研究方向:交通网络优化
    李玉(1990—),女,甘肃兰州人,博士研究生,主要研究方向:物流供应链管理。
  • 基金资助:
    国家自然科学基金资助项目(71671079);甘肃省自然科学基金资助项目(20JR5RA422);天津大学-兰州交通大学自主创新基金合作项目(2020054)

Abstract:

In view of the disadvantages of the standard Artificial Bee Colony (ABC) algorithm such as weak development ability and slow convergence, a new ABC algorithm based on multi-population combination strategy was proposed. Firstly, the different-dimensional coordination and multi-dimensional matching update mechanisms were introduced into the search equation. Then, two combination strategies were designed for the hire bee and the follow bee respectively. The combination strategy was composed of two sub-strategies focusing on breadth exploration and depth development respectively. In the follow bee stage, the population was divided into free subset and non-free subset, and different sub-strategies were adopted by the individuals belonging to different subsets to balance the exploration and development ability of algorithm. The 15 benchmark functions were used to compare the proposed improved ABC algorithm with the standard ABC algorithm and other three improved ABC algorithms. The results show that the proposed algorithm has better optimization performance in both low-dimensional and high-dimensional problems.

Key words: Artificial Bee Colony (ABC) algorithm, update mechanism, combination strategy, population division, balance

摘要:

针对标准人工蜂群(ABC)算法存在开发能力弱、收敛速度慢的缺点,提出了一种基于多种群组合策略的ABC算法。首先,将异维协同和多维匹配的更新机制引入搜索方程;然后,针对雇佣蜂和跟随蜂分别设计了两种组合策略,组合策略是由侧重于广度探索和深度开发的两个子策略构成。在跟随蜂阶段,将种群划分为自由子集和非自由子集,并使属于不同子集的个体采用不同的子策略,从而平衡算法的探索与开发能力。通过15个标准测试函数将所提改进ABC算法与标准ABC算法和其他3种改进ABC算法进行仿真对比,结果表明所提算法在低维和高维问题中都具有更好的寻优性能。

关键词: 人工蜂群算法, 更新机制, 组合策略, 种群划分, 平衡

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