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Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

Author

Listed:
  • Shun Zhou

    (School of Electronic Information, Wuhan University, Wuhan 430072, China
    These author contributed equally to this work.)

  • Yuan Shi

    (School of Electronic Information, Wuhan University, Wuhan 430072, China
    These author contributed equally to this work.)

  • Dijing Wang

    (School of Electronic Information, Wuhan University, Wuhan 430072, China)

  • Xianze Xu

    (School of Electronic Information, Wuhan University, Wuhan 430072, China)

  • Manman Xu

    (School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430072, China)

  • Yan Deng

    (School of Aeronautics and Intelligent Manufacturing, Hankou University, Wuhan 430072, China)

Abstract

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

Suggested Citation

  • Shun Zhou & Yuan Shi & Dijing Wang & Xianze Xu & Manman Xu & Yan Deng, 2024. "Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems," Mathematics, MDPI, vol. 12(10), pages 1-32, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1513-:d:1393542
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    References listed on IDEAS

    as
    1. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    2. Arash Mohammadi Fallah & Ehsan Ghafourian & Ladan Shahzamani Sichani & Hossein Ghafourian & Behdad Arandian & Moncef L. Nehdi, 2023. "Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
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