基于高斯分布的ABC算法及应用

为了克服标准人工蜂群算法中容易陷入局部最优的缺陷、改善寻优过程中随机性过强的缺点,提出一种基于高斯分布的改进人工蜂群算法.通过高斯分布将局部最优和当前全局最优进行比较,从而能较快跳出局部可行区域,并且有较快的收敛速度.最后通过四个常用的数学测试函数进行测试,并将结果和标准ABC、GABC算法进行比较,结果表明改进算法在寻优能力和收敛速度上都有所提高.将改进算法应用于图像边缘检测时,较标准ABC取得了不错的效果.

An Modify Artificial Bee Colony Algorithm Based on Gauss Distribution

In order to overcome the artificial colony algorithm easy to fall into local optimum in the defects, To improve the random more faults in the process of optimization. Proposed a improved artificial bee colony algorithm based on Gauss distribution. By comparing the local optimal and the current global optimal, the Gauss distribution can be quickly out of the local feasible region, and has a faster convergence rate. Finally, four benchmark functions are tested, and compared with the standard ABC algorithm and GABC algorithm, the results show that the improved algorithm can improve the searching ability and convergence speed of the algorithm. The improved algorithm is applied to image edge detection, which has achieved good results compared with the standard ABC.