This project focuses on TSP probelm using Simulated Annealing.
This study investigates the application of Simulated Annealing (SA) to address the Traveling Salesman Problem (TSP). Leveraging the probabilistic approach of SA, the research explores its effectiveness in approximating near-optimal solutions to TSP instances. The results reveal a delicate balance between computational cost and solution quality, underscoring the utility of the algorithm in practical optimization scenarios. Key insights include the impact of SA parameters on convergence and scalability across diverse city configura- tions. The study suggests avenues for future research, emphasizing parameter fine-tuning and broader applications in optimization. The findings enrich the understanding of the role of SA in tackling optimization challenges, particularly in logistics, network design, and resource allocation.
Run all the cells in the Jupyter notebook file TSP.ipynb and Optimal tour.ipynb and get the visual results.
- Python
- Numpy
- Matplotlib
This project is licensed under the MIT License - see the LICENSE file for details.
- Mingjie Li 14823551
- Lingyuan Liu 14821672
- Qiziyi Cao 15056910
Processing: 100%|█████████████████████████████| 10/10 [00:00<00:00, 36.62file/s]
Total commits: 27
Total ctimes: 260
Total files: 10
Total loc: 2583
| Author | loc | coms | fils | distribution |
|:---------|------:|-------:|-------:|:-----------------|
| jazminli | 2583 | 27 | 10 | 100.0/ 100/100.0 |