Artificial bee colony algorithm with multiple search strategies

Wei-feng Gao, Ling-ling Huang, San-yang Liu, Felix T.S. Chan, Cai Dai and Xian Shan

Applied Mathematics and Computation, 2015, vol. 271, issue C, 269-287

Abstract: Considering that the solution search equation of artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation which results in slow convergence, this paper studies whether the performance of ABC can be improved by combining different search strategies, which have distinct advantages. Based on this consideration, we develop a novel ABC with multiple search strategies, named MuABC. MuABC uses three search strategies to constitute a strategy candidate pool. In order to further improve the performance of the algorithm, an adaptive selection mechanism is used to choose suitable search strategies to generate candidate solutions based on the previous search experience. In addition, a candidate solution is generated based on a Gaussian distribution to exploit the search ability. MuABC is tested on a set of 22 benchmark functions, and is compared with some other ABCs and several state-of-the-art algorithms. The comparison results show that the proposed algorithm offers the highest solution quality, the fastest global convergence, and the strongest robustness among all the contenders on almost all the cases.

Keywords: Evolutionary algorithms; Artificial bee colony algorithm; Strategy candidate pool; Gaussian distribution; Search equation (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:271:y:2015:i:c:p:269-287

DOI: 10.1016/j.amc.2015.09.019

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