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<p>This research introduces Kirchhoff’s Law Algorithm (KLA), a novel optimization method inspired by electrical circuit laws, particularly Kirchhoff’s Current Law (KCL). The KLA is evaluated using real-parameter test functions including CEC-2005, 2014, and 2017, comparing its performance with several established algorithms. Results from real-parameter and constrained benchmark functions affirm KLA’s accuracy and convergence rate superiority compared to other algorithms. Notably, when applied to the CEC-2005 benchmarks with dimensions ranging from 30 to 100, KLA demonstrates a remarkable ability to maintain population diversity throughout the search process within a feasible search space. Based on the average rank criteria, KLA consistently outperforms other algorithms despite its simplicity and lack of control parameters (aside from population size). This inherent simplicity makes KLA easy to use as-is, adaptable, and compatible with other optimization techniques. The source codes of the KLA algorithm are publicly available at <a href="https://nimakhodadadi.com/algorithms-%2B-codes">https://nimakhodadadi.com/algorithms-%2B-codes</a>.</p>

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Kirchhoff’s law algorithm (KLA): a novel physics-inspired non-parametric metaheuristic algorithm for optimization problems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月26日

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  • Mojtaba Ghasemi,
  • Nima Khodadadi,
  • Pavel Trojovský,
  • Li Li,
  • Zulkefli Mansor,
  • Laith Abualigah,
  • Amal H. Alharbi,
  • El-Sayed M. El-Kenawy

摘要

This research introduces Kirchhoff’s Law Algorithm (KLA), a novel optimization method inspired by electrical circuit laws, particularly Kirchhoff’s Current Law (KCL). The KLA is evaluated using real-parameter test functions including CEC-2005, 2014, and 2017, comparing its performance with several established algorithms. Results from real-parameter and constrained benchmark functions affirm KLA’s accuracy and convergence rate superiority compared to other algorithms. Notably, when applied to the CEC-2005 benchmarks with dimensions ranging from 30 to 100, KLA demonstrates a remarkable ability to maintain population diversity throughout the search process within a feasible search space. Based on the average rank criteria, KLA consistently outperforms other algorithms despite its simplicity and lack of control parameters (aside from population size). This inherent simplicity makes KLA easy to use as-is, adaptable, and compatible with other optimization techniques. The source codes of the KLA algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.