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Learning 2-opt Heuristics for the TSP via Deep Reinforcement Learning
How to solve the traveling salesman problem with the 2-opt algorithm, a fast heuristic search algorithm.
GraphLab is an application that shows visually how several graph algorithms work
Implementation of the paper A Genetic Algorithm for a Green Vehicle Routing Problem
The research work on local search algorithms
A Travelling Salesman Problem (TSP) solver using a hybrid of strategies
Competitive C++ solution to the Travelling Salesperson 2D problem, that includes the implementation of 6 algorithms: greedy, Clarke-Wright, Christofides, 2-opt, 3-opt, and Lin-Kernighan (k-opt). Done as part of the project assignment in the *DD22440 Advanced Algorithms* course at KTH, by Prof. Danupon Nanongkai.
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?
A simple Quadratic Assignment Problem solver using heuristics and metaheuristics
A Python package for visualizing graph algorithms.
Vehicle Routing Problem optimization with Genetic Algorithm
TSP optimization, Operations Research 2 project, UniPD 2022/23
Podium at Artificial Intelligence Cup 2022 - USI
Multi-storey Vehicle Routing Problem optimization using Iterated Local Search
Vehicle Routing Problem solver with cargo compatibility constraints. Interactive web UI, 2-opt/3-opt optimization, Excel export.
Fully automated, constraint-driven solution to the NP-hard TSP, featuring dynamic package loading with no hardcoded logic. Handles deadlines, groupings, delays, and truck constraints using a multi-phase optimization strategy that minimizes distance while meeting all delivery requirements.
Algorithms Project for Oregon State University
Assignments of Artificial Intelligence Sessional Course CSE 318 in Level-3, Term-2 of CSE, BUET
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