Late parallelization and feedback approaches for distributed computation of evolutionary multi-objective optimization algorithms

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, but also to obtain a higher gain from the use of parallel processors. Therefore, in this study two new parallelization approaches are proposed and discussed, which are named as late parallelization (no-migration approach) and feedback approaches. The performances of these approaches are evaluated on convex and concave multi-objective test problems.

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