Uncertainty Principle based optimization; new metaheuristics framework

TL;DR

This paper introduces a novel metaheuristic optimization framework inspired by the Uncertainty Principle, balancing exploration and exploitation, and demonstrates its effectiveness over existing algorithms through evaluations.

Contribution

The paper proposes a new metaheuristic framework based on Uncertainty Principle concepts, integrating quantum and Fourier analysis insights into optimization algorithms.

Findings

The proposed optimizer outperforms several well-known metaheuristics.

The framework effectively balances exploration and exploitation.

Experimental results validate the algorithm's competency.

Abstract

To more flexibly balance between exploration and exploitation, a new meta-heuristic method based on Uncertainty Principle concepts is proposed in this paper. UP is is proved effective in multiple branches of science. In the branch of quantum mechanics, canonically conjugate observables such as position and momentum cannot both be distinctly determined in any quantum state. In the same manner, the branch of Spectral filtering design implies that a nonzero function and its Fourier transform cannot both be sharply localized. After delving into such concepts on Uncertainty Principle and their variations in quantum physics, Fourier analysis, and wavelet design, the proposed framework is described in terms of algorithm and flowchart. Our proposed optimizer's idea is based on an inherent uncertainty in performing local search versus global solution search. A set of compatible metrics for each part of the framework is proposed to derive preferred form of algorithm. Evaluations and comparisons at the end of paper show competency and distinct capability of the algorithm over some of the well-known and recently proposed metaheuristics.