Adaptive artificial bee colony optimization

In this paper, we propose a novel greedy position update strategy for the ABC algorithm. The greedy position update strategy is implemented mainly in two steps. In the first step, good solutions randomly chosen from the top t solutions in the current population are used to guide the search process of onlooker bees. In the second step, the new parameter t is adaptively adjusted in each iteration of the algorithm. The adjustment is simply based on determining whether the globally best solution is obtained by the employed bees or the onlooker bees. The effect of the proposed greedy position update strategy is evaluated on a set of benchmark functions. Experimental results show that the proposed strategy can significantly improve the performance of the classic ABC algorithm. In addition, ABC using the proposed strategy exhibits very competitive performance when compared with some existing ABC variants.