An Artificial Bee Colony Algorithm Based on Improved Search Strategy

Abstract

Aiming at the shortcomings of the original ABC algorithm, such as initial randomness, prematurity and slow convergence speed, this paper proposes an artificial bee colony algorithm with improved search strategy. In order to increase initial population quality and enhance the overall search ability, it firstly initializes the swarm by using chaotic mapping and reverse learning method. Secondly, the position updating formula based on local better and global best is used to improve the efficiency of the iterative optimization process. Finally, the proposed algorithm is contrasted to PSO algorithm, artificial bee colony algorithm and other improved artificial bee colony optimization algorithms, and the standard test function is used for simulation experiments.