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Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method

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Abstract

Stochastic optimization has been found in many applications, especially for several local optima problems, because of their ability to explore and exploit various zones of the feature space regardless of their disadvantage of immature convergence and stagnation. Whale optimization algorithm (WOA) is a recent algorithm from the swarm-intelligence family developed in 2016 that attempts to inspire the humpback whale foraging activities. However, the original WOA suffers from getting trapped in the suboptimal regions and slow convergence rate. In this study, we try to overcome these limitations by revisiting the components of the WOA with the evolutionary cores of Gaussian walk, CMA-ES, and evolution strategy that appeared in Virus colony search (VCS). In the proposed algorithm VCSWOA, cores of the VCS are utilized as an exploitation engine, whereas the cores of WOA are devoted to the exploratory phases. To evaluate the resulted framework, 30 benchmark functions from IEEE CEC2017 are used in addition to four different constrained engineering problems. Furthermore, the enhanced variant has been applied in image segmentation, where eight images are utilized, and they are compared with various WOA variants. The comprehensive test and the detailed results show that the new structure has alleviated the central shortcomings of WOA, and we witnessed a significant performance for the proposed VCSWOA compared to other peers.

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Authors and Affiliations

  1. Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Abdelazim G. Hussien

  2. Faculty of Science, Fayoum University, Fayoum, Egypt

    Abdelazim G. Hussien

  3. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, People’s Republic of China

    Ali Asghar Heidari & Huiling Chen

  4. Shanghai Lixin University of Accounting and Finance, Shanghai, 201209, People’s Republic of China

    Xiaojia Ye

  5. Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, People’s Republic of China

    Guoxi Liang

  6. The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China

    Zhifang Pan

Authors
  1. Abdelazim G. Hussien
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  2. Ali Asghar Heidari
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  3. Xiaojia Ye
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  4. Guoxi Liang
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  5. Huiling Chen
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  6. Zhifang Pan
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Corresponding authors

Correspondence to Guoxi Liang, Huiling Chen or Zhifang Pan.

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Hussien, A.G., Heidari, A.A., Ye, X. et al. Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method. Engineering with Computers 39, 1935–1979 (2023). https://doi.org/10.1007/s00366-021-01542-0

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