ABSTRACT
Real-world optimization problems are often NP-hard, complex, and CPU time-consuming. Moreover, their modeling evolves continuously in terms of constraints and objectives. Therefore, their resolution requires the use of parallel/distributed hybrid metaheuristics. Unlike exact methods, metaheuristics allow to find sub-optimal solutions in a reasonable execution time. They allow to meet the resolution delays often imposed in the industrial field.
