计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 949-955.DOI: 10.11772/j.issn.1001-9081.2018091984

• 人工智能 • 上一篇    下一篇

增强开发能力的改进人工蜂群算法

张志强, 鲁晓锋, 孙钦东, 王侃   

  1. 西安理工大学 计算机科学与工程学院, 西安 710048
  • 收稿日期:2018-09-26 修回日期:2018-11-27 出版日期:2019-04-10 发布日期:2019-04-10
  • 通讯作者: 张志强
  • 作者简介:张志强(1973-),男,河南巩义人,讲师,博士,主要研究方向:智能计算;鲁晓锋(1976-),男,河南汝州人,副教授,博士,CCF会员,主要研究方向:模式识别、计算机视觉;孙钦东(1975-),男,山东莒南人,教授,博士,CCF高级会员,主要研究方向:智能信息处理、信息安全;王侃(1985-),男,山东济宁人,讲师,博士,主要研究方向:移动通信系统、无线网络虚拟化与资源分配。
  • 基金资助:
    陕西省教育厅自然科学研究项目(18JK0557);陕西省科技统筹创新工程项目(2016KTZDGY05-09);陕西省自然科学基础研究计划青年人才项目(2018JQ6057)。

Improved artificial bee colony algorithm with enhanced exploitation ability

ZHANG Zhiqiang, LU Xiaofeng, SUN Qindong, WANG Kan   

  1. School of Computer Science and Engineering, Xi'an University of Technology, Xi'an Shaanxi 710048, China
  • Received:2018-09-26 Revised:2018-11-27 Online:2019-04-10 Published:2019-04-10
  • Supported by:
    This work is partially supported by the Natural Science Research Program of the Educational Department of Shaanxi Province(18JK0557), the Shaanxi Provincial Science & Technology Co-ordination & Innovation Project Program (2016KTZDGY05-09), the Natural Science Basic Research Plan for Young Talents of Shaanxi Province (2018JQ6057).

摘要: 为解决人工蜂群(ABC)算法收敛速度慢、精度不高和易于陷入局部最优等问题,提出一种增强开发能力的改进人工蜂群算法。一方面,将得出的最优解以两种方式直接引入雇佣蜂搜索公式中,通过最优解指导雇佣蜂的邻域搜索行为,以增强算法的开发或局部搜索能力;另一方面,在旁观蜂搜索公式中结合当前解及其随机邻域进行搜索,以改善算法的全局优化能力。对多个常用基准测试函数的仿真实验结果表明,在收敛速度、精度和全局优化能力等方面,所提算法总体上优于其他类似的ABC算法(例如ABC/best)和集成多种搜索策略的ABC算法(例如ABCVSS(ABC algorithm with Variable Search Strategy)和ABCMSSCE(ABC algorithm with Multi-Search Strategy Cooperative Evolutionary))。

关键词: 群体智能, 人工蜂群算法, 最优解, 邻域搜索

Abstract: The basic Artificial Bee Colony (ABC) algorithm has some shortcomings such as slow convergence, low precision and easily getting trapped in local optimum. To overcome these issues, an improved ABC algorithm with enhanced exploitation ability was proposed. On one hand, the obtained optimum solution was directly introduced into the search equations of employed bees in two different ways and guided the employed bees to perform neighborhood search, which enhanced the exploitation or local search ability of the algorithm. On the other hand, the search was performed by the combination of the current solution and its random neighborhood in the search equations of onlooker bees, which improved the global optimization ability of the algorithm. The simulation results on some common benchmark functions show that in convergence rate, precision, and global optimization or exploration ability, the proposed ABC algorithm is generally better than the other similar improved ABC algorithms such as global best ABC (ABC/best) algorithm, and some ABC algorithms with hybrid search strategy such as ABC algorithm with Variable Search Strategy (ABCVSS) and Multi-Search Strategy Cooperative Evolutionary (ABCMSSCE).

Key words: swarm intelligence, Artificial Bee Colony (ABC) algorithm, optimum solution, neighborhood search

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