Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding  

作　　者：Linguo Li Xuwen Huang Shunqiang Qian Zhangfei Li Shujing Li Romany F.Mansour 

机构地区：[1]School of Computer,Nanjing University of Posts and Telecommunications,Nanjing,210003,China [2]School of Computer and Information Engineering,Fuyang Normal University,Fuyang,236041,China [3]Department of Mathematics,Faculty of Science,New Valley University,El-Kharga,72511,Egypt

出　　处：《Computers, Materials & Continua》2022年第8期3073-3090,共18页计算机、材料和连续体（英文）

基　　金：This paper is supported by the National Youth Natural Science Foundation of China(61802208);the National Natural Science Foundation of China(61572261 and 61876089);the Natural Science Foundation of Anhui(1908085MF207,KJ2020A1215,KJ2021A1251 and KJ2021A1253);the Excellent Youth Talent Support Foundation of Anhui(gxyqZD2019097 and gxyqZD2021142);the Postdoctoral Foundation of Jiangsu(2018K009B);the Foundation of Fuyang Normal University(TDJC2021008).

摘　　要：In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

关 键 词：Coyote optimization algorithm image segmentation multilevel thresholding logistic chaotic map hybrid inverse learning strategy 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]