一种改进的混沌局部搜索的人工蜂群算法

摘要

The artificial bee colony algorithm has good robust , high convergence speed and outstanding performance in global optimization , but its ability of local search is not good enough .In order to improve its ability of local search , an improved chaotic artificial bee colony algorithm was proposed .In the new algorithm, local search is executed nearby the mean of all individuals , selecting the better individual be-tween the solution searched by chaos function and previous population .Experimental simulation shows that the improved algorithm not only accelerates the convergence rate , but also improves its accuracy , whose performance is better than that of the existing artificial bee colony algorithm .%人工蜂群算法具有鲁棒性强、收敛速度快且全局寻优性能优异等优点，但其局部搜索能力不足。为了克服此缺陷，提出了一种改进的混沌局部搜索的人工蜂群算法。新算法在每一代的所有个体的平均值附近利用混沌函数进行局部搜索，然后在搜索到的解和原食物源之间采用贪婪选择的原则确定下一代种群。基于6个标准测试函数的仿真结果表明，本算法能有效地加快收敛速度，提高最优解的精度，其性能优于已有的人工蜂群算法。