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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.18278 |
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| _version_ | 1866913857444773888 |
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| author | Alkhalifa, Rabab Alkhomayes, Fatima Almazroua, Boushra Alhaidan, Dana Alothman, Maryam Almuhaidib, Jumana |
| author_facet | Alkhalifa, Rabab Alkhomayes, Fatima Almazroua, Boushra Alhaidan, Dana Alothman, Maryam Almuhaidib, Jumana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18278 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comparative Review of Parallel Exact, Heuristic, Metaheuristic, and Hybrid Optimization Techniques for the Traveling Salesman Problem Alkhalifa, Rabab Alkhomayes, Fatima Almazroua, Boushra Alhaidan, Dana Alothman, Maryam Almuhaidib, Jumana Distributed, Parallel, and Cluster Computing 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. |
| title | A Comparative Review of Parallel Exact, Heuristic, Metaheuristic, and Hybrid Optimization Techniques for the Traveling Salesman Problem |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.18278 |