The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has attracted the attention of several investigators working on multi-objective optimization. At each iteration, MOEA/D generates an offspring solution from a parent’s neighborhood. The new solution is evaluated, and, according to the decomposition approach, it can replace one or more solutions from the neighborhood, maintaining the population updated. In this sense, MOEA/D can be considered as a steady-state algorithm that maintains updated its population once a new solution is generated. In this work, we investigate the performance of MOEA/D in the multi-objective 0/1 knapsack problem considering a steady-state version and a proposed generational version. We explore the benefits of the generational version proposed in this paper. According to results, we show that the proposed approach can obtain a suitable performance in the multi-objective 0/1 knapsack problem employing between two and eight objective functions. Additionally, we propose a two-stage hybrid algorithm that employs the two different approaches of MOEA/D (i.e., the steady-state and generational versions). Our results reveal that the proposed hybrid approach can outperform the original MOEA/D in the many-objective settings of the 0/1 knapsack problem.
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