Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11889/8340
Title: | Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm | Authors: | Yu, Helong Song, Jiuman Chen, Chengcheng Heidari, Ali Asghar Liu, Jiawen Chen, Huiling Zaguia, Atef Mafarja, Majdi |
Keywords: | Grey wolf optimizer;Mathematical optimization;Nature-inspired algorithms;Salp swarm algorithm;Nonlinear theories;Mathematical optimization;Global optimization;Multi-threshold image segmentation;Kapur’s entropy;Leaf Spot Diseases on Maize | Issue Date: | 2022 | Publisher: | Engineering Applications of Artificial Intelligence | Abstract: | Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the enhanced variants of velocity-free particle swarm optimizer with proven defects and shortcomings in performance. Regardless of the proven defect and lack of novelty in this algorithm, the GWO has a simple algorithm and it may face considerable unbalanced exploration and exploitation trends. However, GWO is easy to be utilized, and it has a low capacity to deal with multi-modal functions, and it quickly falls into the optima trap or fails to find the global optimal solution. To improve the shortcomings of the basic GWO, this paper proposes an improved GWO called multi-stage grey wolf optimizer (MGWO). By dividing the search process into three stages and using different population updating strategies at each stage, the MGWO's optimization ability is improved while maintaining a certain convergence speed. The MGWO cannot easily fall into premature convergence and has a better ability to get rid of the local optima trap than GWO. Meanwhile, the MGWO achieves a better balance of exploration and exploitation and has a rough balance curve. Hence, the proposed MGWO can obtain a higher-quality solution. Based on verification on the thirty benchmark functions of IEEE CEC2017 as the objective functions, the simulation experiments in which MGWO compared with some swarm based optimization algorithms and the balance and diversity analysis were conducted. The results verify the effectiveness and superiority of MGWO. Finally, the MGWO was applied to the multi-threshold image segmentation of Leaf Spot Diseases on Maize at four different threshold levels. The segmentation results were analyzed by comparing each comparative algorithm's PSNR, SSIM, and FSIM. The results proved that the MGWO has noticeable competitiveness, and it can be used as an effective optimizer for multi-threshold image segmentation. | URI: | http://hdl.handle.net/20.500.11889/8340 | DOI: | 10.1016/j.engappai.2021.104653 |
Appears in Collections: | Fulltext Publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm.pdf | 12.27 MB | Adobe PDF | View/Open |
Page view(s)
8
checked on Jan 20, 2024
Download(s)
1
checked on Jan 20, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.