| Citation: | CHEN G，LIN D，CHEN F，et al. Image segmentation based on Logistic regression sparrow algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：636-646 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0268
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The sparrow search algorithm is improved to address its decrease of population diversity in the later stage and its easy fall into the local optimal solution. The improved algorithm introduces the oppositional learning strategy based small hole imaging to update the discoverer’s position, enhancing the diversity of the optimal position. Then, inspired by the Logistic model, a new adaptive factor is proposed to dynamically control the safety threshold, thus balancing the global search and local development capabilities of the algorithm. Simulations of comparison with other algorithms in six benchmark functions are conducted, and experimental results show higher convergence accuracy and speed of the improved algorithm than those of the other algorithms. In engineering applications, the proposed algorithm optimizes the K-means clustering algorithm for image segmentation with satisfactory segmentation performance in terms of peak signal to noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).
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