{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:16:11Z","timestamp":1769714171845,"version":"3.49.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,5]]},"abstract":"<jats:p>Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. L\u00e9vy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.<\/jats:p>","DOI":"10.3233\/jifs-221161","type":"journal-article","created":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T14:33:38Z","timestamp":1662734018000},"page":"365-380","source":"Crossref","is-referenced-by-count":1,"title":["An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation"],"prefix":"10.1177","volume":"44","author":[{"given":"Xiaohan","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, China"},{"name":"School of Electrical Engineering, Suihua University, Suihua, Heilongjiang, China"}]},{"given":"Liangkuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, Heilongjiang, China"}]},{"given":"Bowen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-221161_ref1","doi-asserted-by":"crossref","first-page":"213130","DOI":"10.1109\/ACCESS.2020.3040177","article-title":"A logistic chaotic barnacles mating optimizer with masi entropy for color image multilevel thresholding segmentation","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-221161_ref2","doi-asserted-by":"crossref","first-page":"114766","DOI":"10.1016\/j.eswa.2021.114766","article-title":"Opposition-based Laplacian Equilibrium Optimizer with application in Image Segmentation using Multilevel Thresholding,","volume":"174","author":"Dinkar","year":"2021","journal-title":"Expert Syst. 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