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An Enhanced human evolutionary optimization algorithm for Global optimization and Multi-Threshold Image Segmentation

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BIOMIMETICS 2025年第5期10卷 282-282页

作者： Xiang, Liang Zhao, Xiajie Wang, Jianfeng Wang, Bin Kookmin Univ Grad Sch Techno Design TED Dept Space & Culture Design Seoul 02707 South Korea Hanyang Univ Coll Design Ansan 15588 South Korea

Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing threshold image-segmentation methods suffer from the problem of easily falling into locally optimal thresholds, resulting in poor image segmentation. In order to improve the image-segmentation performance, this study proposes an enhanced human evolutionary optimization algorithm (HEOA), known as CLNBHEOA, which incorporates Otsu's method as an objective function to significantly improve the image-segmentation performance. In the CLNBHEOA, firstly, population diversity is enhanced using the Chebyshev-Tent chaotic mapping refraction opposites-based learning strategy. Secondly, an adaptive learning strategy is proposed which combines differential learning and adaptive factors to improve the ability of the algorithm to jump out of the locally optimum threshold. In addition, a nonlinear control factor is proposed to better balance the global exploration phase and the local exploitation phase of the algorithm. Finally, a three-point guidance strategy based on Bernstein polynomials is proposed which enhances the local exploitation ability of the algorithm and effectively improves the efficiency of optimal threshold search. Subsequently, the optimization performance of the CLNBHEOA was evaluated on the CEC2017 benchmark functions. Experiments demonstrated that the CLNBHEOA outperformed the comparison algorithms by over 90%, exhibiting higher optimization performance and search efficiency. Finally, the CLNBHEOA was applied to solve six multi-threshold image-segmentation problems. The experimental results indicated that the CLNBHEOA achieved a winning rate of over 95% in terms of fitness function value, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM), suggesting that it can be considered

关键词： human evolutionary optimization algorithm CEC 2017 image segmentation learning strategy optimization algorithm

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