Culturizing differential evolution for constrained optimization

In this paper we propose the use of differential evolution as a population space of a cultural algorithm, applied to the solution of constrained optimization problems. Differential evolution is a relatively recent evolutionary algorithm that has been found to be very robust as a search engine for real parameter optimization. Adding different knowledge sources to the variation operator of differential evolution it is possible to improve the search and reduce the computational cost necessary to approximate the global optima of different problems. The proposed technique is validated using a set of well-known constrained optimization problems commonly adopted in the specializad literature. The approach is compared with respect to two techniques that are representative of the state-of-the-art in the area.