Difusco: Graph-based diffusion solvers for combinatorial optimization
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
DeepACO: neural-enhanced ant systems for combinatorial optimization
Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …
The neural network methods for solving Traveling Salesman Problem
Y Shi, Y Zhang - Procedia Computer Science, 2022 - Elsevier
Abstract Traveling Salesman Problem (TSP) is a main attention issue at present. Neural
network can be used to solve combinatorial optimization problems. In recent years, there …
network can be used to solve combinatorial optimization problems. In recent years, there …
UAV trajectory planning for AoI-minimal data collection in UAV-aided IoT networks by transformer
Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted
increasing attention. By taking into account age-of-information (AoI), we investigate the …
increasing attention. By taking into account age-of-information (AoI), we investigate the …
Reducing collision checking for sampling-based motion planning using graph neural networks
Sampling-based motion planning is a popular approach in robotics for finding paths in
continuous configuration spaces. Checking collision with obstacles is the major …
continuous configuration spaces. Checking collision with obstacles is the major …
Glop: Learning global partition and local construction for solving large-scale routing problems in real-time
The recent end-to-end neural solvers have shown promise for small-scale routing problems
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …
From distribution learning in training to gradient search in testing for combinatorial optimization
Extensive experiments have gradually revealed the potential performance bottleneck of
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The …
Learning to solve travelling salesman problem with hardness-adaptive curriculum
Various neural network models have been proposed to tackle combinatorial optimization
problems such as the travelling salesman problem (TSP). Existing learning-based TSP …
problems such as the travelling salesman problem (TSP). Existing learning-based TSP …
Memory-efficient transformer-based network model for traveling salesman problem
Combinatorial optimization problems such as Traveling Salesman Problem (TSP) have a
wide range of real-world applications in transportation, logistics, manufacturing. It has …
wide range of real-world applications in transportation, logistics, manufacturing. It has …
Neural TSP solver with progressive distillation
Travelling salesman problem (TSP) is NP-Hard with exponential search space. Recently, the
adoption of encoder-decoder models as neural TSP solvers has emerged as an attractive …
adoption of encoder-decoder models as neural TSP solvers has emerged as an attractive …