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Found 7 papers, 3 papers with code
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness
The critical node problem (CNP) aims to find a set of critical nodes from a network whose deletion maximally degrades the pairwise connectivity of the residual network.
An effective hybrid search algorithm for the multiple traveling repairman problem with profits
As an extension of the traveling repairman problem with profits, the multiple traveling repairman problem with profits consists of multiple repairmen who visit a subset of all customers to maximize the revenues collected through the visited customers.
Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability.
Targeted sampling of enlarged neighborhood via Monte Carlo tree search for TSP
More precisely, the search process is considered as a Markov decision process (MDP), where a 2-opt local search is used to search within a small neighborhood, while a Monte Carlo tree search (MCTS) method (which iterates through simulation, selection and back-propagation steps), is used to sample a number of targeted actions within an enlarged neighborhood.
Variable Population Memetic Search: A Case Study on the Critical Node Problem
Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems.
A Three-Phase Search Approach for the Quadratic Minimum Spanning Tree Problem
Given an undirected graph with costs associated with each edge as well as each pair of edges, the quadratic minimum spanning tree problem (QMSTP) consists of determining a spanning tree of minimum total cost.
