Auto Adaptive Differential Evolution Algorithm

Abstract

Differential Evolution algorithm has proved to be effective and best method for solving various optimization challenges. It has been proved to be rather cumbersome to manually set control parameters in DE. This paper sketch a new variant of the DE algorithm that provides an environment to auto-adjust the control parameters settings. For the past years, DE has captured the attention in many practical cases. It makes use of a few control parameters that are bound to the same value throughout the evolutionary process. Manual control parameters setting is a time-consuming process, so the proposed work provides a reliable, accurate and fast technique to optimize numerical function. This work is tested against various numerical set functions. Final results show that this proposed algorithm performs a cut above when compared with the classical Differential Evolution algorithm, and the other control parameter setting variant of DE considered in the literature.