A modified Artificial Bee Colony optimizer by comprehensive learning and Powell´ search

T In order to improve the algorithmic ability of balancing the exploration and exploitation tradeoff, a modified Artificial Bee Colony optimizer (MABC) is proposed by combining Powell´s search and comprehensive learning using PSO-based search equation strategy. With comprehensive learning, the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the Powell´s search enables the bees deeply exploit around the promising area, which provides a proper balance between exploration and exploitation. The experimental results on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.