摘要
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,得到了原鲸鱼优化算法在初始种群多样性及寻解遍历性上有所增加,全局搜索能力提高和摆脱局部最优的结果。结果表明,改进算法能得到函数最优值0,0.00030,-3.32;改进算法在寻优能力和稳定性等方面有较大提升,能实现对目标图像精确分割且耗时少。该研究为群智能算法应用于图像分割提供了参考。
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.
作者
周娇
王力
陈小青
ZHOU Jiao;WANG Li;CHEN Xiaoqing(Department of Information and Communication Engineering, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China;School of Information Engineering, Guizhou University of Engineering Science, Bijie 551700, China)
出处
《激光技术》
CAS
CSCD
北大核心
2021年第3期378-385,共8页
Laser Technology
基金
贵州省教育厅创新群体重大研究资助项目(黔财教合[2016]118)
贵州省首批国家级新工科研究与实践资助项目(黔教高函[2018]209号)。
关键词
图像处理
鲸鱼优化算法
最大2维熵
疯狂算子
黄金正弦算法
猫映射
image processing
whale optimization algorithm
2-D maximum entropy
crazy operator
golden sine algorithm
cat mapping
