This is a modern implementation of the Hybrid Genetic Search (HGS) with Advanced Diversity Control of [1], specialized to the Capacitated Vehicle Routing Problem (CVRP).
This algorithm has been designed to be transparent, specialized, and highly concise, retaining only the core elements that make this method successful. Beyond a simple reimplementation of the original algorithm, this code also includes speed-up strategies and methodological improvements learned over the past decade of research and dedicated to the CVRP. In particular, it relies on an additional neighborhood called SWAP*, which consists in exchanging two customers between different routes without an insertion in place.
When using this algorithm (or part of it) in derived academic studies, please refer to the following works:
[1] Vidal, T., Crainic, T. G., Gendreau, M., Lahrichi, N., Rei, W. (2012). A hybrid genetic algorithm for multidepot and periodic vehicle routing problems. Operations Research, 60(3), 611-624. https://doi.org/10.1287/opre.1120.1048 (Available HERE in technical report form).
[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).
We also recommend referring to the Github version of the code used, as future versions may achieve better performance as the code evolves. The version associated with the results presented in [2] is v1.0.0.
There exist wrappers for this code in the following languages:
- C: The C_Interface file contains a simple C API
- Python: The PyHygese package is maintained to interact with the latest release of this algorithm
- Julia: The Hygese.jl package is maintained to interact with the latest release of this algorithm
We encourage you to consider using these wrappers in your different projects. Please contact me if you wish to list other wrappers and interfaces in this section.
This code has been designed to solve the "canonical" Capacitated Vehicle Routing Problem (CVRP). It can also directly handle asymmetric distances as well as duration constraints.
This code version has been designed and calibrated for medium-scale instances with up to 1,000 customers. It is not designed in its current form to run very-large scale instances (e.g., with over 5,000 customers), as this requires additional solution strategies (e.g., decompositions and additional neighborhood limitations). If you need to solve problems outside of this algorithm's scope, do not hesitate to contact me at [email protected].
You need CMake to compile.
Build with:
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -G "Unix Makefiles"
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:
[-nbIterTraces <int>] Number of iterations between traces display during HGS execution. Defaults to 500
[-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
[-nbIterPenaltyManagement <int>] Number of iterations between penalty updates. Defaults to 100
[-targetFeasible <double>] target ratio of feasible individuals between penalty updates. Defaults to 0.2
[-penaltyIncrease <double>] penalty increase if insufficient feasible individuals between penalty updates. Defaults to 1.2
[-penaltyDecrease <double>] penalty decrease if sufficient feasible individuals between penalty updates. Defaults to 0.85
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 progress of the algorithm in the standard output will be displayed as:
It [N1] [N2] | T(s) [T] | Feas [NF] [BestF] [AvgF] | Inf [NI] [BestI] [AvgI] | Div [DivF] [DivI] | Feas [FeasC] [FeasD] | Pen [PenC] [PenD]
[N1] and [N2]: Total number of iterations and iterations without improvement
[T]: CPU time spent until now
[NF] and [NI]: Number of feasible and infeasible solutions in the subpopulations
[BestF] and [BestI]: Value of the best feasible and infeasible solution in the subpopulations
[AvgF] and [AvgI]: Average value of the solutions in the feasible and infeasible subpopulations
[DivF] and [DivI]: Diversity of the feasible and infeasible subpopulations
[FC] and [FD]: Percentage of naturally feasible solutions in relation to the capacity and duration constraints
[PC] and [PD]: Current penalty level per unit of excess capacity and duration
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 HGS-CVRP algorithm from your code.
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -G "Unix Makefiles"
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 --verboseThank you very much for your interest in this code.
This code is still actively maintained and evolving. Pull requests and contributions seeking to improve the code in terms of readability, usability, and performance are welcome. Development is conducted in the dev branch. I recommend to contact me beforehand at [email protected] before any major rework.
As a general guideline, the goal of this code is to stay simple, stand-alone, and specialized to the CVRP. Contributions that aim to extend this approach to different variants of the vehicle routing problem should usually remain in a separate repository. Similarly, additional libraries or significant increases of conceptual complexity will be avoided. Indeed, when developing (meta-)heuristics, it seems always possible to do a bit better at the cost of extra conceptual complexity. The overarching goal of this code is to find a good trade-off between algorithm simplicity and performance.
There are two main types of contributions:
- Changes that do not impact the sequence of solutions found by the HGS algorithm when running
ctestand testing other instances with a fixed seed. This is visible by comparing the average solution value in the population and diversity through a test run. Such contributions include refactoring, simplification, and code optimization. Pull requests of this type are likely to be integrated more quickly. - Changes that impact the sequence of solutions found by the algorithm. In this case, I recommend to contact me beforehand with (i) a detailed description of the changes, (ii) detailed results on 10 runs of the algorithm for each of the 100 instances of Uchoa et al. (2017) before and after the changes, using the same termination criterion as used in 2.
- MIT license
- Copyright(c) 2020 Thibaut Vidal