Metadata-Version: 2.1
Name: py2opt
Version: 1.4.2
Summary: How to solve the traveling salesman problem with the 2-opt algorithm
Home-page: https://github.com/pdrm83/py2opt
Author: Pedram Ataee
Author-email: pedram.ataee@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
License-File: LICENSE.md

[![license](https://img.shields.io/badge/license-MIT-success)](https://github.com/pdrm83/Py2Opt/blob/master/LICENSE.md)
[![doc](https://img.shields.io/badge/docs-Medium-blue)](https://towardsdatascience.com/how-to-solve-the-traveling-salesman-problem-a-comparative-analysis-39056a916c9f)

# 2-Opt Search Algorithm 
In optimization, 2-opt is a simple local search algorithm with a special swapping mechanism that suits well to solve the 
traveling salesman problem. This algorithm is sensitive to the initial point of search, i.e., its final results get 
changed by different initial points. 2-opt runs very fast such that a tsp with 120 cities can be solved in less than 
5 sec on the intel core i7. To get a more reliable result, you should run the 2-opt with different randomized initial 
points for enough number of times. One more thing, the travelling salesman problem has many applications in real world 
such as logistic planning or DNA sequencing. So, having a fast and simple method to solve the TSP is valuable. For more 
detailed description, you can read this article: [How to Solve the Traveling Salesman Problem — A Comparative Analysis](https://towardsdatascience.com/how-to-solve-the-traveling-salesman-problem-a-comparative-analysis-39056a916c9f)

 
## Install
The module requires the following libraries:

* numpy
* random2

Then, it can be installed using pip:
```python
pip install py2opt
```

## Usage
To use this module, you must have a distance matrix `dist_mat` showing the pair distance among all nodes. Then, the 
first thing to do is create an instance of the `RouteFinder` class. When you call the `solve` method, you will get the 
`best_route` and `best_distance` of this problem. Check out the example below.

```python
from py2opt.routefinder import RouteFinder

cities_names = ['A', 'B', 'C', 'D']
dist_mat = [[0, 29, 15, 35], [29, 0, 57, 42], [15, 57, 0, 61], [35, 42, 61, 0]]
route_finder = RouteFinder(dist_mat, cities_names, iterations=5)
best_distance, best_route = route_finder.solve()

print(best_distance)
114
print(best_route)
['A', 'C', 'B', 'D']
```
The solver finds out the optimum order (re: minimum total distance traveled) in which the nodes must be visited along 
with the total distance traveled. Note that the 2-opt algorithm doesn't guarantee the global optimum similar to other 
heuristic search algorithms. So, the results can vary in each iteration.

And that's pretty much it!


