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

Shun Zhou, Yuan Shi, Dijing Wang, Xianze Xu (), Manman Xu () and Yan Deng
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Shun Zhou: School of Electronic Information, Wuhan University, Wuhan 430072, China
Yuan Shi: School of Electronic Information, Wuhan University, Wuhan 430072, China
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

Mathematics, 2024, vol. 12, issue 10, 1-32

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.

Keywords: meta-heuristics; swarm intelligence algorithms; human-based optimization; industrial engineering design problems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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