An Improved MOEA/D Algorithm for Complex Data Analysis

There are a large number of multiple level datasets in the Industry 4.0 era. Thus, it is necessary to utilize artificial intelligence technology for the complex data analysis. In fact, the technology often suffers from the self-optimization issue of multiple level datasets, which is taken as a kind of multiobjective optimization problem (MOP). Naturally, the MOP can be solved by the multiobjective evolutionary algorithm based on decomposition (MOEA/D). However, most existing MOEA/D algorithms usually fail to adapt neighborhood for the offspring generation, since these algorithms have shortcomings in both global search and adaptive control. To address this issue, we propose a MOEA/D with adaptive exploration and exploitation, termed MOEA/D-AEE, which adopts random numbers with a uniform distribution to explore the objective space and introduces a joint exploitation coefficient between parents to generate better offspring. By dynamic exploration and joint exploitation, MOEA/D-AEE improves both global search ability and diversity of the algorithm. Experimental results on benchmark data sets demonstrate that our proposed approach achieves global search ability and diversity in terms of the population distribution than state-of-the-art MOEA/D algorithms.