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Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

文献类型： 外文期刊

作者： Mao, Li 1 ; Mao, Yu 1 ; Zhou, Changxi 1 ; Li, Chaofeng 1 ; Wei, Xiao 3 ; Yang, Hong 3 ;

作者机构： 1.Jiangnan Univ, Sch Internet Things, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China

2.Univ Chinese Acad Sci, Lab Computat Geodynam, Beijing 100049, Peoples R China

3.Chinese Acad Fishery Sci, Freshwater Fisheries Res Ctr, Wuxi 214081, Jiangsu, Peoples R China

期刊名称：MATHEMATICAL PROBLEMS IN ENGINEERING （ 影响因子：1.305； 五年影响因子：1.27 ）

ISSN： 1024-123X

年卷期： 2016 年

页码：

收录情况： SCI

摘要： Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

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