Abstract
In this paper, we propose a new solution selection method to balance the convergence and diversity during the evolutionary process for evolutionary multiobjective optimization. The method sorts the solutions based on their ensemble convergence performance, then selects the solutions based on diversity. The selection method is integrated to the framework of decomposition based multiobjective evolutionary algorithms (MOEAs). In order to demonstrate the performance of the algorithm, it is compared with three classical MOEAs and one state-of-art MOEA. The results indicate that our proposed algorithm is very competitive.
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