Title: Improved multi-strategy artificial bee colony algorithm

Authors: Li Lv; Lieyang Wu; Jia Zhao; Hui Wang; Runxiu Wu; Tanghuai Fan; Min Hu; Zhifeng Xie

Addresses: Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' Jiangxi Expressway Networking Management Center, Nanchang, 330036, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, China; School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

Abstract: Artificial bee colony (ABC) algorithm is a nature-inspired metaheuristic based on imitating the foraging behaviour of bee, which is widely used in solving complex multi-dimensional optimisation problems. In order to overcome the drawbacks of standard ABC, such as slow convergence and low solution accuracy, we propose an improved multi-strategy artificial bee colony algorithm (MSABC). According to the type of position information in ABC, three basic search mechanisms are summarised, the mechanisms include searching around the individual, the random neighbour and the global best solution. Then, the basic search mechanisms are improved to obtain three search strategies. Each bee randomly selects a search strategy to produce a candidate solution under the same probability in each iteration. Thus these strategies can make a good balance between exploration and exploitation. Finally, the experiments are conducted on eight classical functions. Results show that our algorithm performs significantly better than several recently proposed similar algorithms in terms of the convergence speed and solution accuracy.

Keywords: artificial bee colony; ABC; random selection strategy; information interchange; swarm intelligence; metaheuristics; search mechanisms; optimisation; exploration; exploitation; convergence speed; solution accuracy.

DOI: 10.1504/IJCSM.2016.080087

International Journal of Computing Science and Mathematics, 2016 Vol.7 No.5, pp.467 - 475

Received: 16 Jun 2016
Accepted: 08 Aug 2016
Published online: 01 Nov 2016 *

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