MDM-HGS-CVRP is a modified version of HGS-CVRP, an implementation of the Hybrid Genetic Search (HGS) specialized to the Capacitated Vehicle Routing Problem (CVRP) by Thibaut Vidal [2].
It incorporates a new solution generation method into the (re-)initialization process, based on the Multi Data Mining (MDM) approach, which applies patterns extracted from good solutions using data mining, and on a randomized version of the Clarke and Wright savings heuristic.
MDM-HGS-CVRP ranked 2nd in the CVRP track of the 12th DIMACS Implementation Challenge and is described in [1]. The code used in the Challenge (based on HGS-CVRP v1.0.0) is in the dimacs branch.
The main branch is based on HGS-CVRP v2.0.0.
When using this algorithm (or part of it) in derived academic studies, please refer to the following papers:
[1] Maia, M. R. H., Plastino, A., Souza, U. S. (2024). An improved hybrid genetic search with data mining for the CVRP. Networks, 83, 692–711. https://doi.org/10.1002/net.22213 (Available HERE).
[2] Vidal, T. (2022). Hybrid genetic search for the CVRP: Open-source implementation and SWAP* neighborhood. Computers & Operations Research, 140, 105643. https://doi.org/10.1016/j.cor.2021.105643 (Available HERE in technical report form).
This program uses FPmax* LIB.
You should download and build it before compiling the program (see instructions on the link above).
After building FPmax* LIB, copy the required files into the project directory:
- Create a directory named "external" with two subdirectories named "include" and "lib":
mkdir -p external/include external/lib- Copy the required FPmax* LIB header files into the "external/include" directory.
- Copy the FPmax* LIB shared library (.so on Linux or .dll on Windows) into the "external/lib" directory.
You need CMake to compile.
Build with:
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make binThis will generate the executable file hgs in the build directory.
Test with:
ctest -R bin --verboseAfter building the executable, try an example:
./hgs ../Instances/CVRP/X-n157-k13.vrp mySolution.sol -seed 1 -t 30The following options are supported:
Call with: ./hgs instancePath solPath [-it nbIter] [-t myCPUtime] [-bks bksPath] [-seed mySeed] [-veh nbVehicles] [-log verbose]
[-it <int>] sets a maximum number of iterations without improvement. Defaults to 20,000
[-t <double>] sets a time limit in seconds. If this parameter is set, the code will be run iteratively until the time limit
[-seed <int>] sets a fixed seed. Defaults to 0
[-veh <int>] sets a prescribed fleet size. Otherwise a reasonable UB on the fleet size is calculated
[-round <bool>] rounding the distance to the nearest integer or not. It can be 0 (not rounding) or 1 (rounding). Defaults to 1.
[-log <bool>] sets the verbose level of the algorithm log. It can be 0 or 1. Defaults to 1.
Additional Arguments:
[-nbGranular <int>] Granular search parameter, limits the number of moves in the RI local search. Defaults to 20
[-mu <int>] Minimum population size. Defaults to 25
[-lambda <int>] Number of solutions created before reaching the maximum population size (i.e., generation size). Defaults to 40
[-nbElite <int>] Number of elite individuals. Defaults to 5
[-nbClose <int>] Number of closest solutions/individuals considered when calculating diversity contribution. Defaults to 4
[-targetFeasible <double>] target ratio of feasible individuals in the last 100 generatied individuals. Defaults to 0.2
[-randGen <double>] Ratio of randomly generated individuals (complemented using RCW). Dynamic default based on instance size
[-mdmNbElite <int>] Number of individuals in the MDM elite set. Dynamic default based on instance size
[-mdmNbPatterns <int>] Number of (largest) patterns mined from the MDM elite set. Defaults to 5
[-mdmNURestarts <double>] Maximum percentage of restarts without updating the MDM elite set. Defaults to 0.05
[-mdmMinSup <double>] Minimum support of patterns mined from the MDM elite set. Defaults to 0.8
There exist different conventions regarding distance calculations in the academic literature.
The default code behavior is to apply integer rounding, as it should be done on the X instances of Uchoa et al. (2017).
To change this behavior (e.g., when testing on the CMT or Golden instances), give a flag -round 0, when you run the executable.
The main classes containing the logic of the algorithm are the following:
- Params: Stores the main data structures for the method
- Individual: Represents an individual solution in the genetic algorithm, also provides I/O functions to read and write individual solutions in CVRPLib format.
- Population: Stores the solutions of the genetic algorithm into two different groups according to their feasibility. Also includes the functions in charge of diversity management.
- Genetic: Contains the main procedures of the genetic algorithm as well as the crossover
- LocalSearch: Includes the local search functions, including the SWAP* neighborhood
- Split: Algorithms designed to decode solutions represented as giant tours into complete CVRP solutions
- CircleSector: Small code used to represent and manage arc sectors (to efficiently restrict the SWAP* neighborhood)
In addition, additional classes have been created to facilitate interfacing:
- AlgorithmParameters: Stores the parameters of the algorithm
- CVRPLIB Contains the instance data and functions designed to read input data as text files according to the CVRPLIB conventions
- commandline: Reads the line of command
- main: Main code to start the algorithm
- C_Interface: Provides a C interface for the method
You can also build a shared library to call the MDM-HGS-CVRP algorithm from your code.
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make libThis will generate the library file, libhgscvrp.so (Linux), libhgscvrp.dylib (macOS), or hgscvrp.dll (Windows),
in the build directory.
To test calling the shared library from a C code:
make lib_test_c
ctest -R lib --verbose
- MIT license
- Copyright(c) 2020 Thibaut Vidal (HGS-CVRP)
- Copyright(c) 2022 Marcelo Maia