OscaR.cbls is an open source library that proposes a generic API to define a Constraint-Based Local Search (CBLS) solver.
Check our company website for more info about OscaR.cbls: https://asset.cetic.be/en/oscar/
In scala, add this in your build.sbt file:
// Add a resolver linked to the public repository of CETIC.
resolvers += "Sonatype Nexus Repository Manager" at "https://nexus.cetic.be/repository/oscar-public"
// Select the desired oscar-cbls version.
libraryDependencies += "oscar" %% "oscar-cbls_2.13" % "X.Y.Z"See the src/main/scala/oscar/cbls/examples folder to see examples of using OscaR to build problem-solving engines.
A more complete documentation can be found here. An important refactoring is being made for the upcoming version 6.0.0 of OscaR, and as such this documentation is not yet up-to-date. However, it still presents the key concepts and may be useful to understand the way OscaR is meant to be used.
OscaR.cbls was developed in Scala 2.13.14 (check build.sbt), built with SBT 1.9.9
(check project/built.properties) and run with Java 21.
You can use other combinations of compatible versions as mentioned in https://docs.scala-lang.org/overviews/jdk-compatibility/overview.html
Local search is a class of heuristic methods that are used to solve hard optimization problems. In an optimization problem, we want to find a solution that optimizes (minimizes or maximizes) a given objective function, subject to certain constraints. The process of solving a problem with local search starts with an arbitrary initial solution, which is then slightly modified to get a neighboring solution that improves the objective function. Exploring the neighboring solutions is done through a neighborhood.
A key concept of constraint-based local search is to use efficient algorithms to incrementally update the value of the objective function when a neighborhood is explored; these algorithms are then embedded in the constraint representations as well as in invariants, which are objects used to represent the dependencies between variables.
- 3 types of variables:
- IntVariable (an integer)
- SetVariable (a set of integers)
- SeqVariable (a sequence of integers)
- Most common constraints for integers: Sum or Min/Max of an array, Sum, Mult, etc.
- Most common constraints for set: Cardinality, Intersection, Union, etc.
- Most common neighborhoods: Assign a value to a variable, swap two values, etc.
- Rich library for routing problems:
- Most common neighborhoods: One point move, two-opt, three-opt, etc.
- Most common constraints for routing: length of a route, etc.
- Rich API for problem definition
- Rich API for neighborhood and search procedure definition
If you want to contribute, you can create a pull request, which the team will review.
If you have any question, bug or feature request, you can add an issue about it.
NB: Soon available on Maven.