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 established algorithms. Results from real-param-

eter and constrained benchmark functions affirm KLA’s accuracy and convergence rate

superiority compared to other algorithms. Notably, when applied to the CEC-2005 bench-

marks 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.