Traveling Salesman Problem
58 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Traveling Salesman Problem
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Libraries
Use these libraries to find Traveling Salesman Problem models and implementations
TSP/HCP Benchmark set
Most implemented papers
Neural Combinatorial Optimization with Reinforcement Learning
pemami4911/neural-combinatorial-rl-pytorch
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Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.
Differentiation of Blackbox Combinatorial Solvers
martius-lab/blackbox-backprop
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ICLR 2020
Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence.
Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding
These are the Smuggler and Donkeys.
Exploring the Loss Landscape in Neural Architecture Search
In this work, we show that (1) the simplest hill-climbing algorithm is a powerful baseline for NAS, and (2), when the noise in popular NAS benchmark datasets is reduced to a minimum, hill-climbing to outperforms many popular state-of-the-art algorithms.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
yining043/VRP-DACT
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NeurIPS 2021
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
Backpropagation through Combinatorial Algorithms: Identity with Projection Works
martius-lab/solver-differentiation-identity
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Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities.
A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem
Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results.
A Powerful Genetic Algorithm for Traveling Salesman Problem
This paper presents a powerful genetic algorithm(GA) to solve the traveling salesman problem (TSP).
Evolving TSP heuristics using Multi Expression Programming
The results emphasizes that evolved MEP heuristic is a powerful tool for solving difficult TSP instances.
On the Min-cost Traveling Salesman Problem with Drone
The first algorithm (TSP-LS) was adapted from the approach proposed by Murray and Chu (2015), in which an optimal TSP solution is converted to a feasible TSP-D solution by local searches.
