自适应Tent混沌搜索的人工蜂群算法

为了有效改善人工蜂群算法(artificial bee colony algorithm,ABC)的性能,结合Tent混沌优化算法,提出自适应Tent混沌搜索的人工蜂群算法.该算法使用Tent混沌以改善ABC的收敛性能,避免陷入局部最优解,首先应用Tent映射初始化种群,使得初始个体尽可能均匀分布,其次自适应调整混沌搜索空间,并以迄今为止搜索到的最优解产生Tent混沌序列,从而获得最优解.通过对6个复杂高维的基准函数寻优测试,仿真结果表明,该算法不仅加快了收敛速度,提高了寻优精度,与其他最近改进人工蜂群算法相比,其性能整体较优,尤其适合复杂的高维函数寻优.

Artificial bee colony algorithm based on self-adaptive Tent chaos search

In order to improve the performance of artificial bee colony (ABC) algorithm, a novel ABC algorithm based on self-adaptive Tent chaos search which is combined with Tent chaos algorithm is proposed. The algorithm uses Tent chaos mapping to improve the convergence characteristics and prevent the ABC to get stuck on local solutions. In this algorithm, Tent mapping is applied to diversify the initial individuals in the search space. Tent chaotic sequence based an optimal location is produced, and the self-adaptive adjustment of chaos search scopes can obtain the global optima. Experiments on six complex benchmark functions with high-dimension, simulation results further demonstrate that, the improved algorithm not only accelerates the convergence rate and improves solution precision. Compared with other latest improved artificial colony algorithm, it has a better overall performance, especially for complex high-dimensional functions optimization.