摘要： 针对人工蜂群算法易出现早熟现象和收敛速度慢等问题,提出一种基于回溯搜索的人工蜂群算法。通过回溯搜索算法选择更新种群,采用随机的变异策略和不均匀的交叉策略,增强蜂群算法种群多样性,使得改进的蜂群算法能够跳出局部最优,且具有较好的全局收敛速度。将改进的算法用于图像对比度增强,通过搜索非完全Beta函数的最佳参数α,β,确定灰度变换曲线,对图像灰度进行调整,提高图像对比度。仿真实验结果表明,该算法具有较高的求解精度和较快的收敛速度,与直方图均衡化算法相比,有效地增强了图像的对比度。

关键词: 人工蜂群算法, 回溯搜索算法, 种群多样性, 收敛速度, 全局收敛, 图像对比度增强

Abstract: For the problems of premature phenomenon and slow convergence rate appeared in Artificial Bee Colony(ABC) algorithm,an ABC algorithm based on the backtracking search is proposed.The Backtracking Search Algorithm(BSA)is used to select and update the colony.Through the random mutation strategy and non-uniform crossover strategy,the new algorithm can enhance the population diversity of the colony algorithm,enables the algorithm jump out of the local optima and has a better global convergence rate.The improved algorithm is used for image contrast enhancement.By searching the optimal parameters α,β of incomplete Beta function,the gray-scale transformation curve is determined.The image gray level is adjusted to improve the image contrast.Simulation results show that the proposed algorithm,has a higher accuracy and faster convergence rate.Compared with the Histogram Equalization(HE) algorithm,the contrast of the image is enhanced effectively.

Key words: Artificial Bee Colony(ABC) algorithm, Backtracking Search Algorithm(BSA), population diversity, convergence speed, global convergence, image contrast enhancement

中图分类号: 

• TP301.6