A modified Artificial Bee Colony optimizer by comprehensive learning and Powell' search
Boyang Liu, Weiping Shao, Qiuyan Liu, Lianbo Ma · 2015
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.