Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation  

作　　者：Shujing Li Zhangfei Li Wenhui Cheng Chenyang Qi Linguo Li 

机构地区：[1]School of Computer and Information Engineering,Fuyang Normal University,Fuyang,236041,China

出　　处：《Computers, Materials & Continua》2024年第8期2049-2063,共15页计算机、材料和连续体（英文）

基　　金：This work is supported by Natural Science Foundation of Anhui under Grant 1908085MF207,KJ2020A1215,KJ2021A1251 and 2023AH052856;the Excellent Youth Talent Support Foundation of Anhui underGrant gxyqZD2021142;the Quality Engineering Project of Anhui under Grant 2021jyxm1117,2021kcszsfkc307,2022xsxx158 and 2022jcbs043.

摘　　要：To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

关 键 词：Image segmentation image thresholding chimp optimization algorithm chaos initialization Cauchy mutation 

分 类 号：O17[理学—数学]