pALS: An object-oriented framework for developing parallel cooperative metaheuristics

pALS acronym for parallel Adaptive Learning Search is a computational object oriented framework for the development of parallel and cooperative metaheuristics for solving complex optimization problems. The library exploits the paralellization allowing the deployment of mainly two models: the parallel execution of operators and the execution of separate instances or multi-start models. pALS also allows to include in the design of the problem´s solution cooperation strategies such as the islands model for genetic algorithms or the parallel exploration of neighborhoods in metaheuristics derived from local searches, including a broad set of topologies associated with these models. pALS has been successfully used in different optimization problems and has proven to be a flexible, extensible and commanding library to promptly develop prototypes offering a collection of ready to use operators that encompass the nucleus of many metaheuristics including hybrid metaheuristics.