A novel search interval forecasting optimization algorithm

Abstract

In this paper, we propose a novel search interval forecasting (SIF) optimization algorithm for global numerical optimization. In the SIF algorithm, the information accumulated in the previous iteration of the evolution is utilized to forecast area where better optimization value can be located with the highest probability for the next searching operation. Five types of searching strategies are designed to accommodate different situations, which are determined by the history information. A suit of benchmark functions are used to test the SIF algorithm. The simulation results illustrate the good performance of SIF, especially for solving large scale optimization problems. © 2011 Springer-Verlag.