Computational investigation of simple memetic approaches for continuous global optimization

In Locatelli et al. (2014) [20] a memetic approach, called MDE (Memetic Differential Evolution), for the solution of continuous global optimization problems, has been introduced and proved to be quite efficient in spite of its simplicity. In this paper we computationally investigate some variants of MDE. The investigation reveals that the best tested variant of MDE outperforms the original MDE itself, but also that the best variant depends on some properties of the function to be optimized. In particular, a greedy variant of MDE turns out to perform very well over functions with a single-funnel landscape, while another variant, based on a diversity measure applied to the members of the population, works better over functions with a multi-funnel landscape. A hybrid approach is also proposed which combines both the previous variants in order to obtain an overall performance which is good over all functions.