Traveling Salesman Problem
67 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.
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding
These are the Smuggler and Donkeys.
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
qiang-ma/graph-pointer-network
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Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an optimal city permutation under constraints.
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.
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search
ai4co/llm-as-hh
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Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
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.
