https://github.com/juliaconstraints/localsearchsolvers.jl
A Julia package to manage Constraint-Based Local Search (CBLS) solvers.
https://github.com/juliaconstraints/localsearchsolvers.jl
constraint-programming constraint-solver local-search
Last synced: about 2 months ago
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A Julia package to manage Constraint-Based Local Search (CBLS) solvers.
- Host: GitHub
- URL: https://github.com/juliaconstraints/localsearchsolvers.jl
- Owner: JuliaConstraints
- License: mit
- Created: 2020-11-24T01:18:53.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-04-10T00:43:44.000Z (3 months ago)
- Last Synced: 2025-05-07T17:15:03.065Z (about 2 months ago)
- Topics: constraint-programming, constraint-solver, local-search
- Language: Julia
- Size: 1.69 MB
- Stars: 30
- Watchers: 5
- Forks: 0
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LocalSearchSolvers
[](https://JuliaConstraints.github.io/LocalSearchSolvers.jl/dev)
[](https://JuliaConstraints.github.io/LocalSearchSolvers.jl/stable)
[](https://github.com/JuliaConstraints/LocalSearchSolvers.jl/actions)
[](https://codecov.io/gh/JuliaConstraints/LocalSearchSolvers.jl)
[](https://github.com/invenia/BlueStyle)
# Constraint-Based Local Search Framework
The **LocalSearchSolvers.jl** framework proposes sets of technical components of Constraint-Based Local Search (CBLS) solvers and combine them in various ways. Make your own CBLS solver!
A higher-level *JuMP* interface is available as [CBLS.jl](https://github.com/JuliaConstraints/CBLS.jl) and is the recommended way to use this package. A set of examples is available within [ConstraintModels.jl](https://github.com/JuliaConstraints/ConstraintModels.jl).
### Dependencies
This package makes use of several dependencies from the JuliaConstraints GitHub org:
- [ConstraintDomains.jl](https://github.com/JuliaConstraints/ConstraintDomains.jl): a domains back-end package for all JuliaConstraints front packages
- [Constraints.jl](https://github.com/JuliaConstraints/Constraints.jl): a constraints back-end package for all JuliaConstraints front packages
- [CompositionalNetworks.jl](https://github.com/JuliaConstraints/CompositionalNetworks.jl): a module to learn error functions automatically given a *concept*
- [Garamon.jl](https://github.com/JuliaConstraints/Garamon.jl) (incoming): geometrical constraints
It also relies on great packages from the julialang ecosystem, among others,
- [ModernGraphs.jl](https://github.com/Humans-of-Julia/ModernGraphs.jl) (incoming): a dynamic multilayer framework for complex graphs which allows a fine exploration of entangled neighborhoods
### Related packages
- [JuMP.jl](https://github.com/jump-dev/JuMP.jl): a rich interface for optimization solvers
- [CBLS.jl](https://github.com/JuliaConstraints/CBLS.jl): the actual interface with JuMP for `LocalSearchSolvers.jl`
- [ConstraintModels.jl](https://github.com/JuliaConstraints/ConstraintModels.jl): a dataset of models for Constraint Programming
- [COPInstances.jl](https://github.com/JuliaConstraints/COPInstances.jl) (incoming): a package to store, download, and generate combinatorial optimization instances
### Features
Wanted features list:
- **Strategies**
- [ ] *Move*: local move, permutation between `n` variables
- [ ] *Neighbor*: simple or multiplexed neighborhood, dimension/depth
- [ ] *Objective(s)*: single/multiple objectives, Pareto, etc.
- [ ] *Parallel*: distributed and multi-threaded, HPC clusters
- [ ] *Perturbation*: dynamic, restart, pool of solutions
- [ ] *Portfolio*: portfolio of solvers, partition in sub-problems
- [ ] *Restart*
- [x] restart sequence
- [ ] partial/probabilistic restart (in coordination with perturbation strategies)
- [ ] *Selection* of variables: roulette selection, multi-variables, meta-variables (cf subproblem)
- [ ] *Solution(s)*: management of pool, best versus diverse
- [x] *Tabu*
- [x] No Tabu
- [x] Weak-tabu
- [x] Keen-tabu
- [ ] *Termination*: when, why, how, interactive, results storage (remote)
- **Featured strategies**
- [ ] Adaptive search
- [ ] Extremal optimization
- **Others**
- [ ] Resolution of problems
- [x] SATisfaction
- [x] OPTimisation (single-objective)
- [ ] OPTimisation (multiple-objective)
- [ ] Dynamic problems
- [ ] Domains
- [x] Discrete domains (any type of numbers)
- [x] Continuous domains
- [ ] Arbitrary Objects such as physical ones
- [ ] Domain Specific Languages (DSL)
- [x] Straight Julia `:raw`
- [x] JuMP*ish* | MathOptInterface.jl
- [ ] MiniZinc
- [ ] OR-tools ?
- [ ] Learning settings (To be incorporated in [MetaStrategist.jl](https://github.com/JuliaConstraints/MetaStrategist.jl))
- [x] Compositional Networks (error functions, cost functions)
- [ ] Reinforcement learning for above mentioned learning features
- [ ] Automatic benchmarking and learning from all the possible parameter combination (instance, model, solver, size, restart, hardware, etc.)
### Contributing
Contributions to this package are more than welcome and can be arbitrarily, and not exhaustively, split as follows:
- All features mentioned above
- Adding new constraints and symmetries
- Adding new problems and instances
- Adding new ICNs to learn error of existing constraints
- Creating other compositional networks which target other kind of constraints
- Just making stuff better, faster, user-friendlier, etc.
#### Contact
Do not hesitate to contact me (@azzaare) or other members of JuliaConstraints on GitHub (file an issue), the julialang [Discourse](https://discourse.julialang.org) forum, the julialang [Slack](https://julialang.org/slack/) workspace, the julialang [Zulip](https://julialang.zulipchat.com/) server (*Constraint Programming* stream), or the Humans of Julia [Humans-of-Julia](https://humansofjulia.org/) discord server(*julia-constraint* channel).