A multi-leader whale optimization algorithm for global optimization and image segmentation
作者：
Highlights：
• Apply a modified whale optimization algorithm as multi-level image segmentation.
• Memory mechanism and multi-leader are used to enhance exploration ability of WOA.
• Self-learning strategy and levy flight are applied to avoid premature convergence.
• Using Otsu, Kapur entropy, and Fuzzy entropy as fitness functions to assess agents.
• Experimental results show that the proposed approach outperforms other methods.
摘要
•Apply a modified whale optimization algorithm as multi-level image segmentation.•Memory mechanism and multi-leader are used to enhance exploration ability of WOA.•Self-learning strategy and levy flight are applied to avoid premature convergence.•Using Otsu, Kapur entropy, and Fuzzy entropy as fitness functions to assess agents.•Experimental results show that the proposed approach outperforms other methods.
论文关键词：WOA algorithm,Benchmark functions,Multi-level thresholding,Image segmentation,Fuzzy entropy,Otsu method,Kapur's entropy
论文评审过程：Received 3 August 2020, Revised 23 December 2020, Accepted 1 March 2021, Available online 10 March 2021, Version of Record 20 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114841
