A modified differential evolution algorithm with cauchy mutation for global optimization

Abstract

Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution (MDE), a modification to DE that enhances the convergence rate without compromising with the solution quality. In Modified differential evolution (MDE) algorithm, if an individual fails in continuation to improve its performance to a specified number of times then new point is generated using Cauchy mutation. MDE on a test bed of functions is compared with original DE. It is found that MDE requires less computational effort to locate global optimal solution. © 2009 Springer Berlin Heidelberg.