用于指数熵多阈值分割的改进细菌觅食算法

针对图像多阈值分割中阈值搜索是有序正整数规划的特点,提出了一种用于指数熵多阈值分割的改进细菌觅食优化(Improved Bacterial Foraging Optimization,IBFO)算法。首先,将标准的细菌觅食优化(Standard Bacterial Foraging Optimization,SBFO)算法的趋化算子改成动态趋化算子以增强趋化操作的自适应性；然后,将SBFO中的迁徙算子替换成混合随机和动态的迁徙算子,将迁徙过程划分为两个阶段,第一阶段为随机迁徙,目的是增强全局搜索能力,第二阶段为动态局部迁徙,目的是提高局部搜索能力；随后,丢弃SBFO中的感应机制以便加快运行速度；最后,将IBFO算法进一步修改以满足有序正整数规划的要求,并将其应用于指数熵多阈值分割方法中。图像分割实验结果表明,与SBFO,MBFO和IPSO算法相比,提出的IBFO方法不仅优化效果更好,而且运行速度更快。

Improved Bacterial Foraging Optimization Algorithm Used for Multi-level Threshold Segmentation Based on Exponent Entropy

In view of the characteristics of ordered positive integer programming of multi-level segmentation methods,an improved bacterial foraging optimization(IBFO) algorithm used for multi-level threshold segmentation based on exponent entropy was proposed in this paper.Firstly,a chemotactic step mechanism of the standard bacterial foraging optimization(SBFO) algorithm is changed into a dynamic chemotactic step approach to improve self-adaptation.Secondly,the original elimination-dispersal operator is replaced with a new one based on combining random mutation and dynamical local mutation,and the random mutation is used in the first phase to enhance the global search ability and the dynamical mutation is used in the second phase to improve local search performance.Thirdly,the communication mechanism of SBFO is abandoned to accelerate the running speed of the algorithm.Finally,IBFO is further modified to fit for the multi-level threshold segmentation based on exponent entropy.Experimental results show that the proposed method has better optimization performance with less computation time compared to SBFO,MBFO and IPSO.