An Artificial Bee Colony Algorithm with Random Location Updating

作     者：Sun, Lijun Chen, Tianfei Zhang, Qiuwen 

作者机构：Henan Univ Technol Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Henan Peoples R China 

出 版 物：《SCIENTIFIC PROGRAMMING》 (科学程序设计)

年 卷 期：2018年第2018卷第Pt.2期

页      面：1-9页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [U1604151] Outstanding Talent Project of Science and Technology Innovation in Henan Province Program for Scientific and Technological Innovation Team in the Universities of Henan Province [16IRTSTHN029] Science and Technology Project of Henan Province Natural Science Project of the Education Department of Henan Province [18A510001] Fundamental Research funds of the Henan University of Technology [2015QNJH13, 2016XTCX06] 

主　　题：performance characteristics Range searching Particle swarm optimization artificial bee colony algorithm Random activity based costing simulation test honey collection dike swarms information exchange 

摘      要：As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.