This project is still in early development, any feedback is much appreciated!

MHLib.jl is a collection of modules, types, and functions in Julia supporting the effective implementation of metaheuristics and certain hybrid optimization approaches for solving primarily combinatorial optimization problems.

Julia MHLib.jl emerged from the Python mhlib and the older C++ mhlib to which it has certain similarities but also many differences.

The main purpose of the library is to support rapid prototyping and teaching as well as efficient implementations due to Julia's highly effective just-in-time-compilation.

MHLib.jl is developed primarily by the Algorithms and Complexity Group of TU Wien, Vienna, Austria, since 2020.

• Günther Raidl (primarily responsible)
• Nikolaus Frohner
• Thomas Jatschka
• Fabio Oberweger

Major versions of MHLib.jl can be installed from the Julia REPL via

] add MHLib

Development versions are available at https://github.com/ac-tuwien/MHLib.jl and can be installed via

] add https://github.com/ac-tuwien/MHLib.jl.git

Note that MHLib.jl is still behind the capabilities of the Python pymhlib, however, much more performant.

The main module provides the following types for candidate solutions and various functions for them:

• Solution: An abstract type that represents a candidate solution to an optimization problem.
• VectorSolution: An abstract solution encoded by a vector of some user-provided type.
• BoolVectorSolution: An abstract solution encoded by a boolean vector.
• PermutationSolution: An abstract solution representing permutations of a fixed number of elements. _ SubsetVectorSolution: A solution that is an arbitrary cardinality subset of a given set represented in vector form. The front part represents the selected elements, the back part optionally the unselected ones.

Moreover, the main module provides:

• git_version(): Function returning the abbreviated git version string of the current project.
• settings: Global settings that can be defined independently per module in a distributed way, while values for these parameters can be provided as program arguments or in configuration files. Most pymhlib modules rely on this mechanism for their external parameters.

Further modules:

• Schedulers, type Scheduler: A an abstract framework for single trajectory metaheuristics that rely on iteratively applying certain methods to a current solution. Modules like GVNSs and LNSs extend this type towards more specific metaheuristics.
• GVNSs, type GVNSs: A framework for local search, iterated local search, (general) variable neighborhood search, GRASP, etc.
• LNSs, type LNS: A framework for different variants of large neighborhood search (LNS). The selection of the destroy and repair methods is done in an extensible way by means of the abstract type MethodSelector and derived types in order to realize different LNS variants.
• ALNSs, type ALNS: Adaptive large neighborhood search (ALNS). It is realized via LNS and ALNSMethodSelector.

For demonstration purposes subdirectory MHLibDemos provides a package (not officially registered at JuliaHub), with basic implementations for the following classical combinatorial optimization problems, to which some of MHLib's metaheuristics are applied:

• OneMax: basic test problem in which the goal is to set all digits in a binary string to true
• GraphColoring: graph coloring problem based on VectorSolution
• MAXSAT: maximum satisfiability problem based on BinaryVectorSolution
• TSP: traveling salesperson problem based on PermutationSolution
• MKP: multi-constrained knapsack problem based on SubsetVectorSolution
• MISP: maximum independent set problem based on SubsetVectorSolution

It is recommended to take the MHLibDemos package with one of the demos as template for solving your own problem.

Further smaller usage examples can also be found in the test directory of the main package.

Subdirectory Tuning contains examples on how SMAC3 can specifically be used for tuning algorithms implemented in Julia. See Tuning/README.md for details.

See CHANGELOG.md