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Distributing of the multiobjective optimization algorithm into various devices in a parallel fashion is a method for speeding up the computation time of the multiobjective evolutionary algorithms (MOEAs). When the processors are increased in number, the gain from parallelization decreases. Therefore, the aim of the parallelization method is not only to decrease the overall algorithm execution time,...
Evolutionary Multi-objective Optimization aims at finding a diverse set of Pareto-optimal solutions whereof the decision maker can choose the solution that fits best to her or his preferences. In case of limited time (of function evaluations) for optimization this preference information may be used to speed up the search by making the algorithm focus directly on interesting areas of the objective...
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