具有混沌搜索策略的蜂群优化算法

Artificial bee colony algorithm with chaotic-search strategy

提出一种改进人工蜂群局部搜索能力的优化算法,对陷入局部最优值的雇佣蜂,使用禁忌表存储其局部极值,并引入混沌序列重新初始化,在迭代中产生局部极值的邻域点,帮助其逃离束缚并快速搜寻到最优解.改进算法有效地结合标准蜂群算法的全局优化能力、禁忌表的记忆能力和混沌局部搜索能力,对经典函数的测试计算表明,改进算法提高了蜂群寻优能力,在收敛速度和精度上均优于标准蜂群算法,适合工程应用中的复杂函数优化问题.

An advanced artificial bee colony optimization algorithm is presented to enhance the local searching ability.Some employed bees trapped in local optimal solution are initialized again by chaotic series,and the tabu is used table to save the local optimization results in order to introduce neighboring regions of local minimums in the iteration,which helps them break away from local optimum to find the globe optimal solution rapidly.The improved algorithm makes use of the chaotic search to improve the capability of precise search and also keep the ability of global search of artificial bee colony optimization（ABC） algorithm.The experimental results of classic functions show that the algorithm has improved the global optimizing ability,and has great advantage of convergence property and robustness compared to ABC algorithm.