An MOEA/D with multiple differential evolution mutation operators

In evolutionary algorithms, the reproduction operators play an important role. It is arguable that different operators may be suitable for different kinds of problems. Therefore, it is natural to combine multiple operators to achieve better performance. To demonstrate this idea, in this paper, we propose an MOEA/D with multiple differential evolution mutation operators called MOEA/D-MO. MOEA/D aims to decompose a multiobjective optimization problem (MOP) into a number of single objective optimization problems (SOPs) and optimize those SOPs simultaneously. In MOEA/D-MO, we combine multiple operators to do reproduction. Three mutation strategies with randomly selected parameters from a parameter pool are used to generate new trial solutions. The proposed algorithm is applied to a set of test instances with different complexities and characteristics. Experimental results show that the proposed combining method is promising.