py2opt 1.4.2

How to solve the traveling salesman problem with the 2-opt algorithm

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

Install

The module requires the following libraries:

• numpy
• random2

Then, it can be installed using pip:

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.

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!