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Fuzzy hybrid Coyote Optimization Algorithm for Image Thresholding

Computers, Materials & Continua 2022年第8期72卷 3073-3090页

作者： Linguo Li Xuwen Huang Shunqiang Qian Zhangfei Li Shujing Li Romany F.Mansour School of Computer Nanjing University of Posts and TelecommunicationsNanjing210003China School of Computer and Information Engineering Fuyang Normal UniversityFuyang236041China Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

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 *** chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic *** sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization ***,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard *** combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then *** 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 *** on the above improvements,the coyote optimization algorithm has better global convergence and computational *** 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

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