Description of PSO parameters.

Numerical optimization techniques are widely applied across various fields of science and technology, ranging from determining the minimal energy of systems in physics and chemistry to identifying optimal routes in logistics or strategies for high-speed trading. Here, we present a novel method that integrates particle swarm optimization (PSO), a highly effective and widely used algorithm inspired by the collective behavior of bird flocks searching for food, with the physical principle of conserving energy and damping in harmonic oscillators. This physics-based approach allows smoother convergence throughout the optimization process and wider tunability options. We evaluated our method on a standard set of test functions and demonstrated that, in most cases, it outperforms its natural competitors, including the original PSO, as well as commonly used optimization methods such as COBYLA and Differential Evolution.