Quantum-Inspired Genetic Algorithms for Complex Optimization Problems

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired concepts within a genetic algorithm framework to address complex optimization challenges. Traditional genetic algorithms often struggle with slow convergence rates when dealing with intricate problem landscapes. We propose a novel approach that incorporates superposition and entanglement, mimicking key principles from quantum mechanics, into the genetic algorithm's operator design. The core idea is to leverage the ability of quantum systems to explore multiple possibilities simultaneously, thereby accelerating the search for optimal solutions. The algorithm's performance is evaluated against established genetic algorithms on a series of benchmark problems. The results demonstrate the potential of this quantum-inspired approach to significantly improve convergence speed and overall efficiency in tackling complex optimization tasks. Specifically, we analyze the impact of these operators on population diversity and the algorithm's ability to escape local optima. The paper concludes with a discussion of future research directions and potential applications of this hybrid algorithm.

DOI: 10.5281/zenodo.22241343

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