diego-vicente/som-tsp

Health v2 · maintenance only

Flags: no_releases no_readme

Adoption not part of the score

3933 stars · 599 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python implementation that solves the Traveling Salesman Problem using Self-Organizing Maps, a neural network technique adapted to find approximate TSP routes. It provides an approximation algorithm for this NP-complete problem rather than an exact solution.

Use cases

• solve the traveling salesman problem with a self-organizing map
• find approximate TSP routes for city datasets
• learn how SOMs can be applied to combinatorial optimization
• compare TSP approximation heuristics
• visualize how a SOM converges on a route
• benchmark sub-optimal TSP solvers in Python

When to choose

• you need a quick approximate solution to a TSP instance
• you want a readable educational implementation of SOM-based TSP solving
• you are researching neural approaches to combinatorial optimization

When to avoid

• you need a provably optimal TSP solution
• you require high-performance solving of very large instances
• you need a maintained production library with active development

Facets

library · maturity maintenance

machine-learning simulation math artificial-intelligence data-science python cross-platform self-organizing-map traveling-salesman-problem tsp-approximation neural-network optimization algorithms

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