Scalarization approaches are the simplest methods for solving the multiobjective problems. The idea of scalarization is based on decomposition of multiobjective problems into single objective sub-problems. Every one of these sub-problems can be solved in a parallel manner since they are independent with each other. Hence, as a scalarization approach, systematically modification on the desirability levels of the objective values of multiobjective problems can be employed for solving these problems. In this study, desirability function-based scalarization approach is converted into parallel algorithm and applied into seven benchmark problems. The performance of parallel algorithm with respect to sequential one is evaluated based on execution time on different graphical processing units and central processing units. The results show that even the accuracy of parallel and sequential codes are same, the execution time of parallel algorithm is up to 24.5-times faster than the sequential algorithm (8.25-times faster on average) with respect to the complexity of the problem.
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