Many of the optimization problems from the real world are multiobjective in nature, and the reference algorithm for multiobjective optimization is NSGA-II. Frequently, these problems present a high complexity, so classical metaheuristic algorithms fail to solve them in a reasonable amount of time; in this context, parallelism is a choice to overcome this fact to some extent. In this paper we study three parallel approaches (a synchronous and two asynchronous strategies) for the NSGA-II algorithm based on the master-worker paradigm. The asynchronous schemes are designed to be used in grid systems, so they can make use of hundreds of machines. We have applied them to solve a real world problem which lies in optimizing a broadcasting protocol using a network simulator. Our experiences reveal that significant time reductions can be achieved with the distributed approaches by using a grid system of more than 300 processors.
['Juan José Durillo', 'Antonio J. Nebro', 'Francisco Luna', 'Enrique Alba']
A study of master-slave approaches to parallelize NSGA-II