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Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems

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Abstract

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.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  2. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  3. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  4. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  5. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  6. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

  7. School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia

    Laith Abualigah

  8. Depto. de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico

    Diego Oliva

  9. School of Information Engineering, Sanming University, Sanming, 365004, China

    Heming Jia

  10. Department of Electrical Engineering, Aerospace and Avionics, Campus Kamra, Air University, Kamra, Pakistan

    Faiza Gul

  11. Department of Civil and Environmental Engineering, Florida International University, Miami, FL, USA

    Nima Khodadadi

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

    Abdelazim G Hussien

  13. Faculty of Science, Fayoum University, Faiyum, Egypt

    Abdelazim G Hussien

  14. Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan

    Mohammad Al Shinwan

  15. Unit for Data Science and Computing, North-West University, 11 Hoffman Street, 2520, Potchefstroom, South Africa

    Absalom E. Ezugwu

  16. Department of Computer Science, Wenzhou-Kean University, Wenzhou, 325015, China

    Belal Abuhaija

  17. Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, 38044, Abu Dhabi, United Arab Emirates

    Raed Abu Zitar

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  1. Laith Abualigah
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  2. Diego Oliva
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  3. Heming Jia
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  4. Faiza Gul
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  6. Abdelazim G Hussien
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  8. Absalom E. Ezugwu
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  9. Belal Abuhaija
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Correspondence to Laith Abualigah or Belal Abuhaija.

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Abualigah, L., Oliva, D., Jia, H. et al. Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems. Multimed Tools Appl 83, 32613–32653 (2024). https://doi.org/10.1007/s11042-023-16890-w

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