Open Access

ARTICLE

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

by Linguo Li^1,2, Xuwen Huang², Shunqiang Qian², Zhangfei Li², Shujing Li^2,*, Romany F. Mansour³

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

* Corresponding Author: Shujing Li. Email:

Computers, Materials & Continua 2022, 72(2), 3073-3090. https://doi.org/10.32604/cmc.2022.026625

Received 31 December 2021; Accepted 11 February 2022; Issue published 29 March 2022

Abstract

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 (hereinafter 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 algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

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Keywords

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