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Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

Author

Listed:
  • Khizer Mehmood

    (Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan)

  • Naveed Ishtiaq Chaudhary

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Zeshan Aslam Khan

    (Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan)

  • Khalid Mehmood Cheema

    (Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi 46000, Pakistan)

  • Muhammad Asif Zahoor Raja

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Ahmad H. Milyani

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Abdullah Ahmed Azhari

    (The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.

Suggested Citation

  • Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification," Mathematics, MDPI, vol. 10(20), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3821-:d:943893
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    References listed on IDEAS

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    1. Liqiong Huang & Yuanyuan Wang & Yuxuan Guo & Gang Hu, 2022. "An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application," Mathematics, MDPI, vol. 10(13), pages 1-39, July.
    2. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2022. "Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
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    4. Nien-Che Yang & Sun-Wei Liu, 2021. "Multi-Objective Teaching–Learning-Based Optimization with Pareto Front for Optimal Design of Passive Power Filters," Energies, MDPI, vol. 14(19), pages 1-24, October.
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    Cited by:

    1. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Shugang Li & Hui Chen & Xin Liu & Jiayi Li & Kexin Peng & Ziming Wang, 2023. "Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    3. 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.
    4. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    5. Iman Faridmehr & Moncef L. Nehdi & Iraj Faraji Davoudkhani & Alireza Poolad, 2023. "Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm," Mathematics, MDPI, vol. 11(5), pages 1-36, March.
    6. Weixing Lin & Leilei Liu & Guoyan Zhao & Zheng Jian, 2023. "Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock," Mathematics, MDPI, vol. 11(18), pages 1-18, September.
    7. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Khalid Mehmood Cheema & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdulellah Alsulami, 2023. "Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

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