Artificial Bee Colony Algorithm Based on Neighboring Information Learning

Artificial bee colony (ABC) algorithm is one of the most effective and efficient swarm intelligence algorithms for global numerical optimization, which is inspired by the intelligent foraging behavior of honey bees and has shown good performance in most case. However, due to its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. In order to solve this concerning issue, in this paper, we propose a novel artificial bee colony algorithm based on neighboring information learning (called NILABC), in which the employed bees and onlooker bees search candidate food source by learning the valuable information from the best food source among their neighbors. Furthermore, the size of the neighbors is linearly increased with the evolutionary process, which is used to ensure the employed bees and onlooker bees obtain the guidance from the best solution in local area at the early stage and the best solution in the global area at the late stage. Through the comparison of NILABC with the basic ABC and some other variants of ABC on 22 benchmark functions, the experimental results demonstrate that NILABC is better than the compared algorithms on most cases in terms of solution quality, robustness and convergence speed.