A Comparative Analysis of Quantum-Inspired Heuristic Methods for Solving Large-Scale Combinatorial Optimization Problems

Abstract

The increasing complexity of combinatorial optimization problems in various domains necessitates innovative solution approaches. This paper conducts a comparative analysis of quantum-inspired heuristic methods, specifically focusing on their efficacy in tackling large-scale optimization challenges. We employ a robust experimental framework to evaluate the performance of multiple algorithms across standard benchmark problems. Key metrics include convergence speed, solution quality, and computational efficiency. The results reveal that certain quantum-inspired techniques significantly outperform classical approaches, providing not only superior solutions but also reduced processing time. Insights gained from this analysis highlight the critical role of algorithmic design in optimizing combinatorial solutions, paving the way for future advancements in the field of computational intelligence.