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

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 e...

Full description

Saved in:
Bibliographic Details
Published in:The Artificial intelligence review Vol. 58; no. 10; p. 325
Main Authors: Ghasemi, Mojtaba, Khodadadi, Nima, Trojovský, Pavel, Li, Li, Mansor, Zulkefli, Abualigah, Laith, Alharbi, Amal H., El-Kenawy, El-Sayed M.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 26.07.2025
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Behavior
Benchmarks
Chemical reactions
Circuits
Computer engineering
Computer Science
Convergence
Genetic algorithms
Heuristic methods
Law
Methodological problems
Multiculturalism & pluralism
Novels
Optimization
Optimization algorithms
Parameters
Physics
Power supply
Search process
Simplicity
Kirchhoff’s Law Algorithm (KLA)
Engineering problems
Optimization
Optimal engineering design
ISSN:1573-7462, 0269-2821, 1573-7462
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 .
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-025-11289-5