并行精确、启发式、元启发式及混合优化技术关于旅行商问题的比较综述
分布式、并行与集群计算
2025-05-27 v1
摘要
旅行商问题（TSP）是众所周知的NP难组合优化问题，在物流、路由和智能系统中具有广泛应用。由于其阶乘复杂度，求解大规模实例需要可扩展且高效的算法框架，通常由并行计算所支持。本文综述提供了并行TSP优化方法的比较评估，包括精确算法、基于启发式的方法、混合元启发式以及机器学习增强模型。此外，我们引入特定于任务的评估指标，以促进跨范式分析，特别是针对混合和自适应求解器。综述 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.
引用
@article{arxiv.2505.18278,
title = {A Comparative Review of Parallel Exact, Heuristic, Metaheuristic, and Hybrid Optimization Techniques for the Traveling Salesman Problem},
author = {Rabab Alkhalifa and Fatima Alkhomayes and Boushra Almazroua and Dana Alhaidan and Maryam Alothman and Jumana Almuhaidib},
journal= {arXiv preprint arXiv:2505.18278},
year = {2025}
}