基于混沌优化的双种群人工蜂群算法

Bi-Group Artificial Bee Colony Algorithm Based on Chaotic Optimization

为提高人工蜂群算法(ABC)的全局搜索能力,加快收敛速度,提出了一种基于混沌优化的双种群人工蜂群算法(BCABC)。算法将种群随机分为2个种群,在子种群中分别采用不同的选择策略,并通过种群间的信息交互,提高算法的收敛速度。在算法陷入局部最优时,利用混沌思想的遍历性产生新解,跳出局部最优,获得最优解。仿真实验结果表明,改进算法在收敛速度和算法精度上都有明显提高。

This paper proposes a bi-group artificial bee colony（ABC） based on chaotic optimization （BGABC） to improve global search ability of ABC algorithm and accelerate convergence of the algorithm. In this algorithm, all individuals are randomly divided into two populations. Selection methods of the two populations are different. An interchanging information strategy is introduced to accelerate the conver- gence. The ergodic property is used to escape from local optimum. Experiment results show that the con- vergence and accuracy of BGABC algorithm is better than that of the basic ABC.