A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the Pareto front with high accuracy. On the other hand, the NSGA-II algorithm widely used in multiobjective optimization performs nondominated sorting in solution ranking, which means an increase in computational complexity proportional to the square of the population. This execution time becomes a problem in engineering applications. In this paper, we propose distributed, high-speed NSGA-II using a many-core environment to obtain a Pareto-optimal solution set excelling in convergence and diversity. This method improves performance while maintaining the accuracy of the Pareto-optimal solution set by repeating NSGA-II distributed processing in a many-core environment inspired by the divide-and-conquer method together with migration processing for compensation of the nondominated solution set obtained by distributed processing. On comparing with NSGA-II executing on a single CPU and parallel, high-speed NSGA-II using a standard island model, it was found that the proposed method greatly shortened the execution time for obtaining a Pareto-optimal solution set with equivalent hypervolume while increasing the accuracy of solution searching.
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