Modified Artificial Bee Colony Algorithm with Comprehensive Learning Re-initialization Strategy

Artificial bee colony (ABC) algorithm is inspired by the foraging behavior of the honey bee swarm. It has achieved comparable performance to other population-based optimization algorithms. However, the learning mechanism in ABC algorithm is not well balance between exploration and exploitation. In this paper, a comprehensive learning (CL) re-initialization strategy is introduced into original ABC algorithm to enhance the exploration while the best solution of the bee population is used to enhance the exploitation. The modified ABC with CL reinitialization strategy is tested with CEC 2014 benchmark problems and carried out a comparative study with other ABC algorithms and recent state-of-art algorithm. The results show that the proposed ABC-CL algorithm outperforms compared state-of-art algorithms.