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

Shujing Li, Zhangfei Li, Wenhui Cheng, Chenyang Qi, Linguo Li^*
School of Computer and Information Engineering, Fuyang Normal University, Fuyang, 236041, China
* Corresponding Author: Linguo Li. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.051928

Received 18 March 2024; Accepted 21 June 2024; Published online 15 July 2024

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Abstract

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.

Keywords

Image segmentation; image thresholding; chimp optimization algorithm; chaos initialization; Cauchy mutation

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

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