A multiobjective decomposition evolutionary algorithm with optimal history-based neighborhood adaptation and a dual-indicator selection strategy.

Neighborhood selection is an important part of a multiobjective evolutionary algorithm based on decomposition (MOEA/D) because the impetus for population evolution mainly comes from its neighborhood. However, the fixed neighborhood size used in MOEA/D may deteriorate the performance of the algorithm due to an unreasonable allocation of computational resources. To further improve the performance of MOEA/D, this paper proposes a multiobjective decomposition evolutionary algorithm with optimal history-based neighborhood adaptation and a dual-indicator selection strategy. The optimal history-based neighborhood adaptation strategy is applied to alleviate the imbalance between exploration and exploitation in the search process, while the dual-indicator selection strategy is developed to enhance the population diversity. The performance of the proposed algorithm is evaluated on the DTLZ and WFG series test problems. Experimental results show that the proposed algorithm performs competitively in comparison with several MOEA/D variants. [ABSTRACT FROM AUTHOR]