🤖 AI Summary
This paper systematically evaluates parallelization paradigms for large-scale Traveling Salesman Problems (TSP)—a canonical NP-hard combinatorial optimization problem—with respect to scalability and practical deployability. It benchmarks parallel exact methods (e.g., branch-and-bound), classical heuristics, metaheuristics (genetic algorithms, ant colony optimization, simulated annealing), and emerging machine learning–enhanced approaches (including reinforcement learning and quantum-inspired methods). The study introduces a task-specific evaluation framework for hybrid and adaptive solvers, moving beyond conventional single-metric (accuracy/time) assessments. It rigorously characterizes trade-offs among solution quality, computational efficiency, problem-scale scalability, and robustness across paradigms. Key research gaps are identified, and a reproducible benchmarking infrastructure—designed for heterogeneous computing and distributed architectures—is proposed. This work provides methodological foundations for intelligent logistics and real-time route planning.
📝 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.