Improving artificial Bee colony algorithm using a new neighborhood selection mechanism

Abstract Artificial bee colony (ABC) and its most modifications use a probability method to select good food sources (called solutions) in the onlooker bee search phase. However, the probability selection does not work with increasing of iterations, because the fitness values cannot be used to distinguish two different solutions. In order to tackle this problem, this paper proposes a new ABC (called NSABC), in which a new selection method based on neighborhood radius is used. Unlike the probability selection in the original ABC, NSABC chooses the best solution in the neighborhood radius to generate offspring. Based on the neighborhood radius, two new solution search strategies are modified. The scout bee search phase is improved by using opposition-based learning and the neighborhood radius. To evaluate the search ability of NSABC, there are 22 benchmark problems used in the experiments. Performance comparison shows NSABC achieves better results than five other ABC algorithms.