Artificial bee colony based on adaptive search strategy and random grouping mechanism

As a popular global optimization algorithm, artificial bee colony (ABC) has strong search ability and simple concept. However, ABC has some deficiencies. The exploitation ability of ABC is not as strong as its exploration ability. The original roulette selection in the onlooker bee search will gradually lose its effect with increasing of iterations. In order to tackle the above problems, an efficient ABC based on adaptive search strategy and random grouping mechanism (called ASRGABC) is proposed in this paper. Firstly, an adaptive search strategy is designed by comparing the success rate of the current and previous iterations. According to the changes of the success rate, a suitable search strategy is adaptively selected. Then, a random grouping mechanism is proposed to replace the original roulette selection. The whole population is randomly divided into several groups. The onlooker bees are allowed to follow the best solution in each group. Based on the random grouping, the search strategy is modified. Thirdly, opposition-based learning is employed to enhance the scout bee phase. To verify the performance of ASRGABC, 22 classical benchmark problems and 28 CEC 2013 benchmark problems are tested. Experimental results show ASRGABC obtains better performance than thirteen other ABC variants.