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The novel multi-swarm coyote optimization algorithm for automatic skin lesion segmentation

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Abstract

Coyote optimization algorithm (COA) is one of population-based swarm intelligence algorithms inspired by the swarming behavior of coyotes. However, COA showed its effectiveness in solving the global optimization problem, it suffers from premature convergence and stagnation in local optima, espicially in a complex space. In this paper, the multi-swarm topology is employed, where the population is divided into several sub-swarms. The performance of multi-swarm coyote optimization algorithm (MCOA) is evaluated on a set of benchmark functions provided in the IEEE CEC 2005 and IEEE CEC 2017 special sessions. Also, it is evaluated for solving multi-level thresholding problem, where 44 skin dermoscopic images obatined from PH2 benchmark dataset are used. The experimental results showed that employing mutli-swarm topology can significantly improve the population diversity and thus the exploration ability. Also, the results reveal that proposed MCOA has the advantages of remarkable stability and high accuracy compared with its classical version and other state-of-art meta-heuristic optimization algorithms. Additionally, a new skin lesion segmentation model based on MCOA is proposed as well. The results illustrate the effectiveness and efficiency of the proposed model and it can be further used for skin disease diagnosis and treatment planning.

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Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Gehad Ismail Sayed

  2. Faculty of Computers and Information, Helwan University, Helwan, Egypt

    Ghada Khoriba & Mohamed H. Haggag

Authors
  1. Gehad Ismail Sayed
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  2. Ghada Khoriba
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  3. Mohamed H. Haggag
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Correspondence to Gehad Ismail Sayed.

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Appendix list of benchmark functions

Appendix list of benchmark functions

See Tables 11, 12, and 13.

Table 11 Definition o IEEE CEC 2005 benchmark functions
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Table 12 Properties of IEEE CEC 2005 benchmark functions, ub denotes upper bound, lb denotes lower bound, opt denotes optimum point and dim denotes dimensions
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Table 13 IEEE CEC 2017 benchmark functions
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Sayed, G.I., Khoriba, G. & Haggag, M.H. The novel multi-swarm coyote optimization algorithm for automatic skin lesion segmentation. Evol. Intel. 17, 679–711 (2024). https://doi.org/10.1007/s12065-020-00450-4

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