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The ParadisEO-PEO Framework

0.1

-intro

+Introduction ParadisEO is a white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is one of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency.

In practice, combinatorial optimization problems are often NP-hard, CPU time-consuming, and evolve over time. Unlike exact methods, metaheuristics allow to tackle large-size problems instances by delivering satisfactory solutions in a reasonable time. Metaheuristics are general-purpose heuristics that split in two categories: evolutionary algorithms (EA) and local search methods (LS). These two families have complementary characteristics: EA allow a better exploration of the search space, while LS have the power to intensify the search in promising regions. Their hybridization allows to deliver robust and better solutions

Although serial metaheuristics have a polynomial temporal complexity, they remain unsatisfactory for industrial problems. Parallel and distributed computing is a powerful way to deal with the performance issue of these problems. Numerous parallel and distributed metaheuristics (PDM) and their implementations have been proposed, and are available on theWeb. They can be reused and adapted to his/her own problems. However, the user has to deeply examine the code and rewrite its problem-specific sections. The task is tedious, errorprone, takes along time and makes harder the produced code maintenance. A better way to reuse the code of existing PDM is the reuse through libraries. These are often more reliable as they are more tested and documented. They allow a better maintainability and efficiency. However, libraries do not allow the reuse of design.