Learning 2-opt Heuristics for the TSP via Deep Reinforcement Learning
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Updated
Oct 20, 2020 - Python
Learning 2-opt Heuristics for the TSP via Deep Reinforcement Learning
GraphLab is an application that shows visually how several graph algorithms work
How to solve the traveling salesman problem with the 2-opt algorithm, a fast heuristic search algorithm.
Assignments of Artificial Intelligence Sessional Course CSE 318 in Level-3, Term-2 of CSE, BUET
Implementation of the paper A Genetic Algorithm for a Green Vehicle Routing Problem
Discrete Optimization Algorithms
A Travelling Salesman Problem (TSP) solver using a hybrid of strategies
The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?
The research work on local search algorithms
A small app for creating the optimal roundtrip between up to 11 places. Uses Nearest-Neighbour-Algorithm to find upper bound and 2-Opt to optimize route. Written in February 2017 for a Code Competition sponsored by Hermes.
A Python package for visualizing graph algorithms.
Attempt at solving the travelling salesman problem by implementing a 2 opt solution
Algorithms Project for Oregon State University
Qt Application to solve the TSP problem using TSPLIB instances and applied in Google Maps, through hybridization of GRASP and VNS metaheuristics
Code from seminars and homework, second year in the university
Discrete and continuous optimization problems solved iteratively and approximately by metaheuritic algorithms.
Implementing travelling salesman in python
Solving the traveling salesman problem using the Gurobi Solver, the farthest insertion algorithm, the nearest neighbor algorithm and, finally, using the 2-opt optimization method.
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