Iterative hybridization of DE with local search for the CEC'2015 special session on large scale global optimization

Continuous optimization is an important research field because many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. Into continuous optimization, solving high-dimensional optimization problems, also called large scale optimization, is a difficult challenge by the huge expansion of the domain search with the dimensionality. In this paper we propose a new hybrid algorithm to tackle this type of optimization, combining a DE with a LS method in a iterative way. The sharing of the best solution between these components in combination with a memory making possible a more in-depth search in each one of them, allowing the algorithm to obtain good results. Experiments are carried out using a benchmark designed for large scale optimization, and the proposal is compared with other algorithms, showing that the algorithm is robust, obtaining good results specially in the most difficult functions.