【摘要】 针对人工蜂群算法(Artificial Bee Colony algorithm,ABC)因直接采用函数值映射的概率选择食物源而引起过早收敛和陷入局部最优以及优化精度不高的问题,提出一种基于排名映射概率的混沌人工蜂群算法(Chaotic Artificial Bee Colony algorithm based on Rank mapping probability,CABC-R)。首先利用目标函数值的排名映射获取选择食物源的概率,然后构建基于排名映射概率的人工蜂群算法以便能够维持种群的多样性,获得较好的全局最优解,最后创建较高寻优精度的新型局部混沌优化算法精确寻找最优解。对10个标准测试函数进行了仿真,结果表明,CABC-R算法不仅优化效果更准确而且更能跳出局部最优,有效地找到全局最优解,优于标准的ABC、JADE、MSEP和RABC算法。更多还原

【Abstract】 In view of the shortcomings of artificial bee colony algorithms,such as the low convergence rate and being trapped into local optimums owing to choosing the food source based on direct mapping probability,and low optimization precision,a chaotic artificial bee colony optimization algorithm based on rank mapping probability(CABC-R)was proposed in this paper.The proposed search process was divided into two different phases:in the first one an ABC global optimizer based on rank mapping probability was created to get a global solution,in the second one the local chaotic optimization algorithm was gotten to obtain more precise an optimum.The simulation results on 10standard test complicated functions indicate that the proposed optimization algorithm is rapid and effective,and that it outperforms the current global optimization algorithms such as ABC,JADE,MSEP and RABC.更多还原