Improved artificial bee colony algorithm based on damping motion and artificial fish swarm algorithm
Liyi Zhang, Mingyue Fu, Hongbo Li, Ting Liu · Journal of Physics Conference Series · 2021
Abstract The basic artificial bee colony (ABC) algorithm is easy to fall into local optimum, and it has poor exploitation ability and slow convergence speed. According to the defects, the improved algorithm is proposed, which is based on damping motion and artificial fish swarm(AFS) algorithm. In the employed bees phase, the swarm behavior of AFS algorithm which can avoid sinking into local optimum is introduced. At the same time, considering the slow convergence speed, employed bees adopt the multidimensional updating process in the early stage of iteration. In the onlooker bees phase, an adaptive step size based on damping motion is designed to replace the random step size, which can balance the global exploration and the local exploitation. Through the simulation of six test functions, we get the average convergence algebra, the mean, the best and the variance of the optimal solution through 30 experiments. Simulation shows that the improved algorithm has a better performance than the basic one.