ABSTRACT
Metaheuristics allow to provide near-optimal solutions of NP-hard complex problems in a reasonable time. They fall into two complementary categories: evolutionary algorithms (EAs) that have a good exploration power, and local searches (LSs) characterized by better intensification capabilities. The hybridization of the two categories permits to improve the effectiveness (quality of provided solutions) and the robustness of the metaheuristics [11]. Nevertheless, as it is CPU time consuming it is not often fully exploited in practice. Indeed, experiments with hybrid metaheuristics are often stopped before the convergence is reached. Nowadays, Peer-to-Peer (P2P) computing [8] and grid computing [5] are two powerful ways to achieve high performance on long-running scientific applications. Parallel hybrid metaheuristics used for solving real-world multiobjective problems (MOPs) are good challenges for P2P and grid computing. However, to the best of our knowledge no research work has been published on that topic.
