An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy

Inspired by the natural phenomenon that honey bees follow the elite group in the foraging process, we propose a novel artificial bee colony algorithm named ECABC based on elite group guidance and the combined breadth-depth search strategy in this paper. Firstly, by simulating the behavior that bees follow the elite group, a novel neighborhood search equation is proposed. According to the equation, with the center of the elite group as the starting point of the search, under the guidance of the global optimum, neighborhood search is performed. Secondly, a combined search strategy is designed as follows: the stochastic breadth-first search strategy for employed bees and the stochastic depth-first search strategy for onlooker bees. Thirdly, the random selection method of elite bees is adopted to replace the probability selection method of onlooker bees. Besides, the influencing parameters of the optimization results are studied and the optimum parameters allowing the best comprehensive performance are obtained. In addition, the proposed algorithm is experimentally verified with 22 benchmark functions and then compared with other improved artificial bee colony algorithms. The comparison results show that the ECABC can effectively improve the convergence speed, convergence precision, and robustness.