Now showing 1 - 2 of 2
  • Publication
    Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems
    (2023)
    Abualigah, Laith
    ;
    Oliva, Diego
    ;
    Jia, Heming
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    Gul, Faiza
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    Khodadadi, Nima
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    Hussien, Abdelazim G
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    Al Shinwan, Mohammad
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    Ezugwu, Absalom E.
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    Abuhaija, Belal
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    Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.
      22
  • Publication
    Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation
    (2023)
    Abualigah, Laith
    ;
    Habash, Mahmoud
    ;
    Hanandeh, Essam Said
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    Hussein, Ahmad MohdAziz
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    Al Shinwan, Mohammad
    ;
    ;
    Jia, Heming
    This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.
      11