"Neural Combinatorial Optimization with Reinforcement Learning"[Bello+, 2016], Traveling Salesman Problem solver

Some lecture notes of Operations Research (usually taught in Junior year of BS) can be found in this repository along with some Python programming codes to solve numerous problems of Optimization including Travelling Salesman, Minimum Spanning Tree and so on.

Concorde is a computer code for the symmetric traveling salesman problem (TSP) and some related network optimization problems. It was created by David Applegate, Robert E. Bixby, Vašek Chvátal, and William J. Cook.

OptFrame - C++17/C++20/C++23 Optimization Framework in Single or Multi-Objective. Supports classic metaheuristics and hyperheuristics: Genetic Algorithm, Simulated Annealing, Tabu Search, Iterated Local Search, Variable Neighborhood Search, NSGA-II, Genetic Programming etc. Examples for Traveling Salesman, Vehicle Routing, Knapsack Problem, etMu

Reinplemtation of paper "A reinforcement learning approach for optimizing multiple traveling salesman problems over graphs"

Solution to TSP (Travelling salesman problem) using Particle Swarm Optimization (PSO) - Language: Python

A C++ Ant Colony Optimization (ACO) algorithm for the traveling salesman problem.

Solving Travelling Salesman Problem using Ant Colony Optimization

An Ant Colony Optimization algorithm for the Traveling Salesman Problem

Solve the travelling salesman problem using a mixed integer optimization algorithm with JuMP

multi traveling salesman problem with ant colony optimization

Python implementation of Tabu Search (TB), Genetic Algorithm (GA), and Simulated Annealing (SA) solving Travelling Salesman Problem (TSP). Term project of Intelligent Optimization Methods, UCAS course 070105M05002H. 禁忌搜索, 遗传算法, 模拟退火解旅行商问题的Python实现. 中国科学院大学现代智能优化方法大作业.

A Reinforcement Learning Approach for Optimizing Multiple Traveling Salesman Problems over Graphs

An optimization app for the Traveling Salesman Problem

Hybrid Pointer Networks for Traveling Salesman Problems Optimization

A Python implementation of the Ant Colony Optimization algorithm for generating solutions to such problems as the Traveling Salesman Problem.

A population based stochastic algorithm for solving the Traveling Salesman Problem.

Optimal facility location (Set Cover problem); setup costs and changeover times (traveling salesman problem) ; production and transportation optimization; LP; MIP; GUROBI 7.5.2 Python API

An attempt at using as many as possible COOL computer science stuff to produce a single image (Lindenmayer system, Penrose tiling, Travelling Salesman Problem, Ant Colony Optimization, A*, etc.).

Ant Colony Optimization for the Travelling Salesman Problem

Travelling Salesman Problem using Ant Colony Optimization

A gcode optimizer for solving the traveling salesman problem with G0

Codes and instances used for Paper 'Modeling and Solving the Traveling Salesman Problem for a Plug-in Hybrid Electric Vehicle Considering Speed Optimization"

Multiagent optimization system (MAOS) for solving the Traveling Salesman Problem (TSP).

This repository contains the implementation of the Traveling Salesman Problem with Quantum Approximate Optimization Algorithm.

This repository provides a solution to the classic Traveling Salesman Problem (TSP) using a genetic algorithm. The genetic algorithm is a heuristic optimization method inspired by the process of natural selection and genetic variation.

You can use this package for Travelling Salesman Problem-related optimization problems such as Route Planning, Delivery Management etc. I implemented all important operators of Genetic Algorithm: Crossover, Mutation, Elitism, Tournament Selection etc. I created a dataset which contains 20 points, and each point has X-Y coordinates. You can use this dataset or any dataset which is in the same form without major modifying in the code.

Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals’ physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems. This file contains the MATLAB code of the algorithm, the test functions and the TSP application.

Solving Travelling Salesman Problem using Ant Colony Optimization

Particle swarm optimization (PSO)-based algorithm For the traveling salesman problem (TSP)