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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.