基于改进鲸鱼优化算法的最大2维熵图像分割

为了避免原鲸鱼优化算法早熟收敛、易陷入局部最优等缺陷,首先在原鲸鱼优化算法初始化过程中采用了猫映射产生混沌序列结合反向解方法取代随机产生初始种群;其次在位置更新机制上采用了疯狂算子和黄金正弦算法的方法;最后将改进鲸鱼优化算法用于寻求图像2维最大熵来确定图像分割最佳阈值的选取.对10个经典基准函数进行了试验仿真验证,得到...

Image segmentation of 2-D maximum entropy based on the improved whale optimization algorithm

In order to avoid the early convergence of the original whale optimization algorithm and easily fall into the local optimum, firstly, the cat mapping generation chaotic sequence combined with the reverse solution method was used to replace the randomly generated initial population in the process of initializing the original whale optimization algorithm. Secondly, the crazy operator and the golden sine algorithm were used in the position updating mechanism. Finally, the improved whale optimization algorithm was used to find the maximum entropy of 2-D image to determine the optimal threshold of image segmentation. The simulation results of 10 classical benchmark functions show that the original whale optimization algorithm can increase the initial population diversity and search ergodicity, and improve the global search ability and get rid of the local optimum. The results show that the optimal value of the function is 0, 0.00030, -3.32.The improved algorithm can achieve accurate segmentation of target image and less time consuming. This study provides a reference for the application of group intelligence algorithms to image segmentation.