Displaying 1 to 13 from 13 results

som-tsp - Solving the Traveling Salesman Problem using Self-Organizing Maps

  •    Python

This repository contains an implementation of a Self Organizing Map that can be used to find sub-optimal solutions for the Traveling Salesman Problem. The instances of the problems that the program supports are .tsp files, which is a widespread format in this problem. All the source code can be found in the src directory, while a report and brief presentation slides (in Spanish) can be found in the report folder. However, for a complete read on the topic, you can read my blog post explaining this implementation and its evaluation. This code is licensed under MIT License, so feel free to modify and/or use it in your projects. If you have any doubts, feel free to contact me or contribute to this repository by creating an issue.

OsmSharp

  •    DotNet

OsmSharp is a routing and OSM data processing library.

graph - Graph algorithms

  •    Python

Graph algorithms. MST (Boruvka, Kruskal, Prim), TSP (double-tree, Christofides, ILP formulation + cutting planes), Eulerian path

node-or-tools - Node.js bindings for or-tools vehicle routing problems

  •    C++

NodeJS bindings for or-tools Travelling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) solvers.See API.md for documentation.




asn1crypto - Python ASN.1 library with a focus on performance and a pythonic API

  •    Python

A fast, pure Python library for parsing and serializing ASN.1 structures. Python has long had the pyasn1 and pyasn1_modules available for parsing and serializing ASN.1 structures. While the project does include a comprehensive set of tools for parsing and serializing, the performance of the library can be very poor, especially when dealing with bit fields and parsing large structures such as CRLs.

Pants - A Python implementation of the Ant Colony Optimization Meta-Heuristic

  •    Python

Pants provides you with the ability to quickly determine how to visit a collection of interconnected nodes such that the work done is minimized. Nodes can be any arbitrary collection of data while the edges represent the amount of "work" required to travel between two nodes. Thus, Pants is a tool for solving traveling salesman problems. The world is built from a list of nodes and a function responsible for returning the length of the edge between any two given nodes. The length function need not return actual length. Instead, "length" refers to that the amount of "work" involved in moving from the first node to the second node - whatever that "work" may be. For a silly, random example, it could even be the number of dishes one must wash before moving to the next station at a least dish-washing dish washer competition.

js-aco - A visual demo of Ant Colony Optimisation applied to TSP written in Javascript

  •    Javascript

JS ACO is a visual demo of Ant Colony Optimisation written in Javascript (ES6). It contains basic example of how ACO works given a randomly generated TSP. The basic parameters of the Ant System are available to be tuned. The UI will show the current optimum tour, and also a heat map of the heuristic (distance) matrix plus a pheromone matrix. This information will update as the algorithm runs.

TSP - Traveling Salesperson Problem - R package

  •    R

This package provides the basic infrastructure and some algorithms for the traveling salesman problems (symmetric, asymmetric and Euclidean TSPs). The package provides some simple algorithms and an interface to the Concorde TSP solver and its implementation of the Chained-Lin-Kernighan heuristic. Current development version: Download package from AppVeyor or install from GitHub (needs devtools).


tsp - Solutions to the Traveling Salesman Problem

  •    Python

Two implementations of solutions to the Traveling Salesman Problem in Python 3. The first solution brute forces all permutations and is guaranteed to find the optimal solution for visiting all points.

timestamp - Time-Stamp Protocol (TSP) implementation for Go as specified in RFC3161

  •    Go

The package timestamp implements the Time-Stamp Protocol (TSP) as specified in RFC3161 (Internet X.509 Public Key Infrastructure Time-Stamp Protocol (TSP)).

Genetic-Algorithm-for-the-Traveling-Salesman-Problem - A population based stochastic algorithm for solving the Traveling Salesman Problem

  •    Java

A population based stochastic algorithm for solving the Traveling Salesman Problem. This program is also capable of producing a 2D heat map for determining the optimal parameters but I have not included an image of this.

TSP - Traveling Salesman Problem Using Parallel Genetic Algorithms

  •    CSharp

Travelling salesman problem (TSP) has been already mentioned in one of the previous chapters. To repeat it, there are cities and given distances between them.Travelling salesman has to visit all of them, but he does not to travel very much. Task is to find a sequence of cities to minimize travelled distance. In other words, find a minimal Hamiltonian tour in a complete graph of N nodes. I do not have any degree in GA so this article can't be used as GA book or GA tutorial. There aren't any mathematics nor logic nor algebra about GA. It's only a programmer's view on Genetic Algorithms and only example of GA coding. Use it carefully! Any comments and criticism are highly appreciated.

ntnu-som - Using Self-Organizing Maps for Travelling Salesman Problem

  •    Python

Self-organizing maps (SOM) or Kohonen maps are a type of artificial neural network (ANN) that mixes in an interesting way the concepts of competitive and cooperative neural networks. A SOM behaves as a typical competitive ANN, where the neurons fight for a case. The interesting twist added by Kohonen is that when a neurons wins a case, the prize is shared with its neighbors. Typically, the neighborhood is bigger at the beginning of the training, and it shrinks in order to let the system converge to a solution. One of the most interesting applications of this technique is applying it to the Travelling Salesman Problem, in which we can use a coordinate map and trace a route using the neurons in the ANN. By defining weight vectors as positions in the map, we can iterate the cities and treat each one as a case that can be won by a single neuron. The neuron that wins the case gets it weight vector updated to be closer to the city, but also its neighbors get updated. The neurons are placed in a 2D space, but they are only aware of a single dimension in their internal ANN, so their behavior is like an elastic ring that will eventually fit all the cities in the shortest distance possible.






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