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Balancing individual and collective strategies: A new approach in metaheuristic optimization

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  • Cuevas, Erik
  • Vásquez, Mario
  • Avila, Karla
  • Rodriguez, Alma
  • Zaldivar, Daniel

Abstract

Metaheuristic approaches commonly disregard the individual strategies of each agent within a population, focusing primarily on the collective best solution discovered so far. While this methodology can yield promising results, it also has several significant drawbacks, such as premature convergence. This study introduces a new metaheuristic approach that emphasizes the balance between individual and social learning in agents. In this approach, each agent employs two strategies: an individual search technique performed by the agent and a social or collective strategy involving the best-known solution. The search strategy is considered a learning problem, and agents must adjust the use of both individual and social strategies accordingly. The equilibrium of this adjustment is determined by a counter randomly set for each agent, which determines the frequency of use invested in each strategy. This mechanism promotes diverse search patterns and fosters a dynamic and adaptive process, potentially improving problem-solving efficiency in intricate spaces. The proposed method was assessed by comparing it with several well-established metaheuristic algorithms using 21 test functions. The results demonstrate that the new method surpasses popular metaheuristic algorithms by offering superior solutions and attaining quicker convergence.

Suggested Citation

  • Cuevas, Erik & Vásquez, Mario & Avila, Karla & Rodriguez, Alma & Zaldivar, Daniel, 2025. "Balancing individual and collective strategies: A new approach in metaheuristic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 227(C), pages 322-346.
  • Handle: RePEc:eee:matcom:v:227:y:2025:i:c:p:322-346
    DOI: 10.1016/j.matcom.2024.08.004
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    References listed on IDEAS

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    1. John Silberholz & Bruce Golden, 2010. "Comparison of Metaheuristics," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 625-640, Springer.
    2. Tammy Harris & James W. Hardin, 2013. "Exact Wilcoxon signed-rank and Wilcoxon Mann–Whitney ranksum tests," Stata Journal, StataCorp LP, vol. 13(2), pages 337-343, June.
    3. Marco Dorigo & Thomas Stützle, 2019. "Ant Colony Optimization: Overview and Recent Advances," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 311-351, Springer.
    4. S. Taheri & G. Hesamian, 2013. "A generalization of the Wilcoxon signed-rank test and its applications," Statistical Papers, Springer, vol. 54(2), pages 457-470, May.
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