# tsp-grasp
Welcome to `tsp-grasp`! A Python package with Heuristics for solving the Traveling Salesman Problem (TSP).
[Documentation](https://tsp-grasp.readthedocs.io/en/latest/)
<p align="center">
<img src="./assets/grasp_arc_horizontal.gif" width="600" title="icon tsp">
</p>
## Install
The package `tspgrasp` is available on PyPI, so you can install it via `pip` by running the following command line:
```
pip install tspgrasp
```
To install the package in development versions, clone it from github using:
```
git clone https://github.com/bruscalia/tsp-grasp.git
```
And run one of the two following commands:
```
pip install .
```
```
python setup.py install
```
Make sure Cython and numpy are also available in your python environment.
Alternatively, you can directly run:
```
pip install git+https://github.com/bruscalia/tsp-grasp.git
```
Here is a minimum working example in which a symmetric distance matrix is produced from 2-dimensional coordinates.
## Use
```python
# Imports
import numpy as np
from scipy.spatial.distance import pdist, squareform
from tspgrasp import Grasp
```
```python
# Create distance matrix
X = np.random.random((100, 2))
D = squareform(pdist(X))
# Instantiate algorithm and solve problem
grasp = Grasp(seed=12)
sol = grasp(D, time_limit=10, max_iter=100)
# Display cost and tour
print(f"Cost: {sol.cost}")
print(f"Tour: {sol.tour}")
```
## Theory
Greedy Randomized Adaptive Search Procedures (GRASP) are metaheuristics constituted by a hybridization of a semi-greedy procedure with a local search method. For more details please refer to Resende & Ribeiro (2016).
Local search moves implemented were based on the work of Vidal (2022).
## Contact
You can reach out to me at bruscalia12@gmail.com
## References
Resende, M. G., & Ribeiro, C. C. (2016). Optimization by GRASP. Springer Science+ Business Media New York. https://doi.org/10.1007/978-1-4939-6530-4
Vidal, T. (2022). Hybrid genetic search for the CVRP: Open-source implementation and SWAP* neighborhood. Computers & Operations Research, 140, 105643. https://doi.org/10.1016/j.cor.2021.105643
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"keywords": "Traveling Salesman Problem,TSP,Greedy Randomized Adaptive Search Procedures,Heuristics,Metaheuristics,Meta-heuristics,Greedy Algorithms,Local Search,Variable Neighborhood Search,Simulated Annealing",
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"description": "# tsp-grasp\n\nWelcome to `tsp-grasp`! A Python package with Heuristics for solving the Traveling Salesman Problem (TSP).\n\n[Documentation](https://tsp-grasp.readthedocs.io/en/latest/)\n\n<p align=\"center\">\n <img src=\"./assets/grasp_arc_horizontal.gif\" width=\"600\" title=\"icon tsp\">\n</p>\n\n## Install\n\nThe package `tspgrasp` is available on PyPI, so you can install it via `pip` by running the following command line:\n\n```\npip install tspgrasp\n```\n\nTo install the package in development versions, clone it from github using:\n\n```\ngit clone https://github.com/bruscalia/tsp-grasp.git\n```\n\nAnd run one of the two following commands:\n\n```\npip install .\n```\n\n```\npython setup.py install\n```\n\nMake sure Cython and numpy are also available in your python environment.\n\nAlternatively, you can directly run:\n\n```\npip install git+https://github.com/bruscalia/tsp-grasp.git\n```\n\nHere is a minimum working example in which a symmetric distance matrix is produced from 2-dimensional coordinates.\n\n## Use\n\n```python\n# Imports\nimport numpy as np\nfrom scipy.spatial.distance import pdist, squareform\nfrom tspgrasp import Grasp\n```\n\n```python\n# Create distance matrix\nX = np.random.random((100, 2))\nD = squareform(pdist(X))\n\n# Instantiate algorithm and solve problem\ngrasp = Grasp(seed=12)\nsol = grasp(D, time_limit=10, max_iter=100)\n\n# Display cost and tour\nprint(f\"Cost: {sol.cost}\")\nprint(f\"Tour: {sol.tour}\")\n```\n\n## Theory\n\nGreedy Randomized Adaptive Search Procedures (GRASP) are metaheuristics constituted by a hybridization of a semi-greedy procedure with a local search method. For more details please refer to Resende & Ribeiro (2016).\n\nLocal search moves implemented were based on the work of Vidal (2022).\n\n\n## Contact\n\nYou can reach out to me at bruscalia12@gmail.com\n\n## References\n\nResende, M. G., & Ribeiro, C. C. (2016). Optimization by GRASP. Springer Science+ Business Media New York. https://doi.org/10.1007/978-1-4939-6530-4\n\nVidal, T. (2022). Hybrid genetic search for the CVRP: Open-source implementation and SWAP* neighborhood. Computers & Operations Research, 140, 105643. https://doi.org/10.1016/j.cor.2021.105643\n",
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