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arXiv · 2505.18278

A Comparative Review of Parallel Exact, Heuristic, Metaheuristic, and Hybrid Optimization Techniques for the Traveling Salesman Problem

Rabab Alkhalifa·Fatima Alkhomayes·Boushra Almazroua·Dana Alhaidan·Maryam Alothman·Jumana Almuhaidib
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Abstract

The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem with wide-ranging applications in logistics, routing, and intelligent systems. Due to its factorial complexity, solving large-scale instances requires scalable and efficient algorithmic frameworks, often enabled by parallel computing. This literature review provides a comparative evaluation of parallel TSP optimization methods, including exact algorithms, heuristic-based approaches, hybrid metaheuristics, and machine learning-enhanced models. In addition, we introduce task-specific evaluation metrics to facilitate cross-paradigm analysis, particularly for hybrid and adaptive solvers. The review concludes by identifying research gaps and outlining future directions, including deep learning integration, exploring quantum-inspired algorithms, and establishing reproducible evaluation frameworks to support scalable and adaptive TSP optimization in real-world scenarios.

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Rabab Alkhalifa, Fatima Alkhomayes, Boushra Almazroua, Dana Alhaidan, Maryam Alothman, Jumana Almuhaidib. 2025-05-23. A Comparative Review of Parallel Exact, Heuristic, Metaheuristic, and Hybrid Optimization Techniques for the Traveling Salesman Problem. https://arxiv.org/abs/2505.18278

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