A Novel Search Strategy Based On Gradient And Distribution Information For Artificial Bee Colony Algorithm

The artificial bee colony (ABC) algorithm is a heuristic optimization algorithm inspired by the foraging behavior of honey bee swarm. The different-based update strategy has caused the slow convergence and precocious phenomena for the ABC algorithm. In order to effectively solve the problems, a novel update strategy based on the gradient and distribution information of the population has been proposed in this study. The improved ABC algorithm makes use of the gradient to guide the population for discovering the potential optimal solution quickly. With the increasing of the number of function evaluations, a distribution-based update strategy also has been used to keep the diversity of the population. The performance of the proposed ABC algorithm was examined on well-known benchmark functions. The experimental results show that the proposed algorithm is more efficient than the basic ABC algorithm and some improved ABC algorithms in terms of the solution quality, convergence and robustness.