I developed this project to delve into Genetic Algorithms and their application to optimization problems. Feel free to explore the code, run the algorithm, and share your feedback.

This repository offers solutions to the Traveling Salesman Problem through two algorithms: Dynamic Programming for precise results and the K-opt Heuristic for fast, near-optimal routes. Whether you prioritize accuracy or speed, our codebase simplifies TSP optimization for various applications.

travelling salesman problem using ortools

A simple heuristic construction for solving the traveling salesman problem (TSP) in Python.

Ant Colony Optimizer devoted to solving TSP.

Genetic Algorithm Optimizer devoted to solving TSP.

Ant Colony Optimization for Traveling Salesman Problem

Efficient TSP solver using Simulated Annealing for Iran's provincial centers. Python code for route optimization and visualization. Explore shortest routes!

Comparison between a genetic algorithm and simulated annealing approach to the traveling salesman problem

This project implements a graph algorithm provinding a suboptimal solution to the knapsak problem in a geographical context. The decisions are driven by the maximization of a regional score. The node with the best regional score is selected as the next node to visit. The algorithms process recursively to find the K Best Nodes (KBN) to visit.

Optimization algorithms and heuristics

Simulated Annealing Optimizer devoted to solving TSP.

Using Ant-Colony Algorithms to solve Travelling salesman problem (TSP).

Travelling salesman problem with Genetic algorithm

Applying Genetic Algorithm to solve the travelling salesman problem

Genetic algorithm for the Travelling salesman problem

Proof of concept about DEAP in Python in order to solve TSP

traveling salesman optimization using simulated annealing

The Traveling Salesman Problem (TSP) aims for the most efficient route across cities. This Python project, employing Tkinter and the Model-View-Controller (MVC) architecture, visualizes TSP solutions. It generates random cities, utilizes the nearest neighbor algorithm, and presents the optimal path with a Tkinter-based GUI.

Framework de algoritmos para TSP