Optimization of Dynamic Systems: A Comparative Study of Two Modified Differential Evolution Algorithms

Abstract

Differential Evolution (DE) is a novel, efficient, effective and robust evolutionary optimization technique. DE algorithm is gaining popularity for solving complex optimization problems encountered in many engineering disciplines. This paper deals with the application and performance evaluation of two modified versions of DE [named as Modified Differential Evolution (MDE) and Trigonometric Differential Evolution (TDE)]. Three algorithms have been used for solving five dynamic optimization problems encountered in chemical engineering. Results indicate that MDE algorithm outperformed DE and TDE algorithms for solving problems with small number of control stages. But TDE algorithm is found to be faster and efficient than MDE and DE algorithms particularly for problems involving large number of control stages.

Keywords: Evolutionary computation, Dynamic optimization, Optimal control, Chemical processes, Differential Evolution (DE), Modified Differential Evolution. INTRODUCTION