一种基于混沌搜索改进的人工蜂群算法

An Improved Artificial Bee Colony Algorithm Based on Chaos Search

针对人工蜂群算法存在容易陷入局部最优解以及收敛速度慢的不足,提出了一种改进的人工蜂群算法.该算法在基本人工蜂群算法的基础上,根据可能解上的适应值定义搜索步长来解决陷入局部最优的问题,根据可调的参数定义食物源选择概率模型来解决收敛速度慢的问题,该选择的概率模型是基于混沌搜索定义全局最优解的搜索方法.最后,在7种不同的测试函数上对改进后的算法进行了实验测试,实验结果表明该改进算法比基本人工蜂群算法有着更高的搜索精度和较低的时间复杂度.

As the deficiency of local optimum solution and slow speed of convergence in artificial bee colony algorithm,an improved artificial bee colony algorithm has been proposed.This algorithm is based on the basic artificial bee colony algorithm.To solve the deficiency of local optimum solution,the step length for searching is defined based on the fitness value of possible solution.To solve the deficiency of slow speed of convergence,aprobabilistic model for choosing a food is defined by an adjustable parameter.Based on the probabilistic model,the chaos search is used to find a globally optimal solution.At last,this improved algorithm is experimentally tested on seven different test functions.The results show that the improved algorithm has a higher search accuracy and a lower time complexity than the basic artificial bee colony algorithm.The improved algorithm is more effective in solving function optimization problems with a faster convergence rate.