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This paper deals with the use of parallel processing for multi-objective optimization in applications in which the objective functions, the restrictions, and hence also the solutions can change over time. These dynamic optimization problems appear in quite different real-world applications with relevant socio-economic impact. The procedure in this paper is presented based on PSFGA, a parallel evolutionary procedure for multi-objective optimization. It uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). Moreover, the procedure exclusively uses non-dominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.