Abstract
A hybrid algorithm is proposed for multiobjective optimization in this paper. The proposed algorithm consists of multiobjective evolutionary algorithm based on decomposition (MOEA/D) and recurrent neural network, where MOEA/D is for global search while recurrent neural network is for local search. The performance of the proposed algorithm is compared with other three multi-objective algorithms in terms of hypervolume and inverted generational distance. The performance investigation shows that the proposed algorithm generally outperforms the compared algorithms.
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