An Effective Large Neighborhood Search Heuristic for the Generalized Traveling Salesman Problem
Author stephenlsmith
9 Stars
Updated Last
12 Months Ago
Started In
October 2019


GLNS is a solver for the Generalized Traveling Salesman Problem (GTSP), implemented in Julia (

More information on the solver is given at

Citing this work

The GLNS solver and its settings are described in the following paper [DOI] [PDF]:

	author =    {S. L. Smith and F. Imeson},
	title =     {{GLNS}: An Effective Large Neighborhood Search Heuristic
	             for the Generalized Traveling Salesman Problem},
	journal =   {Computers \& Operations Research},
	volume =    87,
	pages =     {1-19},
	year =      2017,

Please cite this paper when using GLNS.

Using the solver

The solver can be run from the command line or from the Julia REPL.


Begin by installing Julia v1.0 or higher from

GLNS can then be installed through the Julia package manager:

julia> using Pkg
julia> Pkg.add("GLNS")

Once installed, import the package and run as follows:

julia> import GLNS
julia> GLNS.solver("<path_to_instance>", options)

The input to GLNS is a text file in GTSPLIB format, which is an extension of the TSPLIB format.

Three example inputs are given in the examples directory of the repository.

Solver usage and examples

GLNS has three default settings: slow, default, and fast. It also has several flags that can be used to give to give the solver timeout, or to have it quit when a solution cost threshold is met.

Example 1: Solving the instance "39rat195.gtsp" using the default settings. The solver settings, tour cost, and tour are outputted to the file "tour.txt" (written to the working directory).

julia> GLNS.solver("examples/39rat195.gtsp", output = "tour.txt")

The file "tour.txt" can then be parsed to extract the tour and its cost.

Example 2: Running the solver with the "slow" setting.

julia> GLNS.solver("test/39rat195.gtsp", mode="slow")

Example 3: The solver is set to run persistently (achieved by setting trials to a large number) for at most 60 seconds, but will quit if it finds a tour of cost 13,505 or less. The best known solution for this instance is 13,502.

julia> GLNS.solver("test/107si535.gtsp", max_time=60, budget=13505, trials=100000)

Running from the command line

Julia has a startup time of approximately 0.5 seconds, and so this option has a delay at each call when compared to running directly through Julia. However, this option may be preferable if interfacing with code written in another language like Python or MATLAB.

After installing the package as described above, download the command line solver GLNScmd.jl from this repository and place in a convenient location.

The syntax is:

$ <path_to_script>/GLNScmd.jl <path_to_instance> -options

The following are a few examples:

$ <path_to_script>/GLNScmd.jl test/39rat195.gtsp
$ <path_to_script>/GLNScmd.jl test/39rat195.gtsp -mode=fast -output=tour.txt

# GLNS can also be set to run "persistently" for a given amount of time.
# The following example will run for 60 seconds before terminating.
$ <path_to_script>/GLNScmd.jl test/39rat195.gtsp -max_time=60 -trials=100000

Index of files

The GLNS solver package is arranged as follows.

  • GLNScmd.jl -- Command line solver
  • examples/ -- contains sample GTSP instances for testing and as example inputs
  • src/ -- contains
    • GLNS.jl --- Main Julia solver
    • adaptive_powers.jl
    • insertion_deletion.jl
    • parameter_defaults.jl
    • parse_print.jl
    • tour_optimizations.jl
    • utilities.jl
  • test/ -- test scripts for installation verification


Copyright 2018 Stephen L. Smith and Frank Imeson

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Contact information

Prof. Stephen L. Smith
Department of Electrical and Computer Engineering
University of Waterloo
Waterloo, ON Canada