关键词: 群体智能, 人工蜂群, 优化, 摄动因子, 基准函数

Abstract: An improved algorithm based on Artificial Bee Colony (ABC) algorithm was proposed to solve the problem that traditional ABC algorithm is inclined to fall into local optima. In the first stage, the improved ABC algorithm was derived from the skills of extrapolation in mathematics to update the new location of ABC. In the second stage, in order to overcome the deficiency of high position similarity in later stage of evolution and slow renewal rate and enhance the ability of local search in feasible region, a fine-tuning mechanism was introduced to ABC. Simultaneously, the effect of convergence subjected to different perturbation factors was discussed. Finally, the simulation results in three benchmark functions show that the proposed algorithm has better performance than traditional algorithm in search ability and accuracy.

Key words: swarm intelligence, Artificial Bee Colony (ABC), optimization, perturbation factor, bechmark function