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An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

Author

Listed:
  • Ghareeb Moustafa

    (Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia
    Electrical Engineering Department, Suez Canal University, Ismailia 41522, Egypt)

  • Ali M. El-Rifaie

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Idris H. Smaili

    (Electrical Engineering Department, Jazan University, Jazan 45142, Saudi Arabia)

  • Ahmed Ginidi

    (Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Abdullah M. Shaheen

    (Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ahmed F. Youssef

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Mohamed A. Tolba

    (Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo 11787, Egypt)

Abstract

This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA’s core concept is inspired by the dwarf mongoose’s foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy.

Suggested Citation

  • Ghareeb Moustafa & Ali M. El-Rifaie & Idris H. Smaili & Ahmed Ginidi & Abdullah M. Shaheen & Ahmed F. Youssef & Mohamed A. Tolba, 2023. "An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3297-:d:1203470
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    References listed on IDEAS

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