Artificial bee colony algorithm with multiple search strategies

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.