TSPLIB - A Traveling Salesman Problem Library

By Gerhard Reinelt. 1991

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Learning Combinatorial Optimization Algorithms over Graphs (2017, methodology): “We use the standard TSPLIB library [32] which is publicly available 6 .”
On the Min-cost Traveling Salesman Problem with Drone (2015, methodology): “This set, along with the remaining nodes that are not appeared in any selected drone routes, is then used to construct a symmetry TSPLIB file [9] in line 2.”
Chained Lin-Kernighan for Large Traveling Salesman Problems (2003, background): “Wereport computational results for the TSPLIB set of test instances collected by Reinelt (1991,1995), as well as results for random geometric instances having up to 25,000,000 cities.”
A comprehensive benchmark set and heuristics for the traveling thief problem (2014, background): “The TSP library collected by Reinelt [2, 10] is to be the main departure point when one is going to evaluate his/her approach’s performance on the TSP.”
MIRPLib - A library of maritime inventory routing problem instances: Survey, core model, and benchmark results (2014, methodology): “We call this libary MIRPLib in the spirit of other libraries in the OR community such as TSPLib [43], MIPLib [35], ORLib [7], MineLib [24], and LINER-LIB [10], which have been used for the traveling salesman problem, mixed-integer linear programming (MILP), OR, open-pit mining, and liner shipping ne”
A new branch-and-cut algorithm for the capacitated vehicle routing problem (2004, methodology): “They are all Euclidean, with edge costs rounded to integers according to the TSPLIB standard [41].”
Particle swarm optimization-based algorithms for TSP and generalized TSP (2007, methodology): “19 instances from the TSPLIB library [24] have been selected to examine the validity of the proposed algorithm for solving GTSP problems.”
An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems (2016, methodology): “For the TSP, 22 instances have been used, and they have been obtained from the TSPLIB Benchmark ([Reinelt(1991)]).”
The p-median problem: A survey of metaheuristic approaches (2005, background): “The larger problem instances are usually taken from the travelling salesman library, Reinelt (1991). They are available at the TSP-Lib webpage2.”
Guided Local Search (2010, unknown): “We tested GLS+FLS+2Opt against LK (Voudouris and Tsang, 1999) in a set of benchmark problems from the public TSP library (Reinelt, 1991).”
A Hybrid Heuristic for the p-Median Problem (2004, methodology): “Originally proposed for the traveling salesman problem, they are available from the TSPLIB [29].”
Discrete cuckoo search algorithm for the travelling salesman problem (2014, methodology): “In Reinelt [24], all these TSP instances belong to the Euclidean distance type.”
Deep Reinforcement Learning for Multiobjective Optimization (2019, methodology): “Test Set: The standard TSP test problems kroA and kroB in the TSPLIB library [37] are used to construct the Euclidean test instances kroAB100, kroAB150, and kroAB200 which are commonly used MOTSP test instances [12], [13]. kroA and kroB are two sets of different city locations and used to calculate…”
RevLib: An Online Resource for Reversible Functions and Reversible Circuits (2008, methodology): “The benefits of widely used benchmark collections have already been seen e.g. in the circuit domain with ISCAS benchmarks [4], in the area of Boolean satisfiability by SATLIB [9] and by TSPLIB [15] for the traveling salesman problem.”
Search Algorithms for Regression Test Case Prioritization (2007, methodology): “The K–Optimal approach has been studied in the area of heuristic s earch to solve the Travelling Salesman Problem (TSP) that is defined as “ find the cycle of minimum cost that visits each of the vertices of a weighted graph G at least once ” [16].”