A Modified Trigonometric Differential Evolution Algorithm for Optimization of Dynamic Systems

Differential Evolution (DE) is a novel evolutionary algorithm capable of handling non-differentiable, nonlinear and multimodal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary optimization method. Still it takes large computational time for solving the computationally expensive objective functions (for example optimization problems in the areas of computational mechanics, computational fluid dynamics, optimal control etc.) And therefore, an attempt to speed up DE is considered necessary. This paper deals with application and evaluation of a modified version of Trigonometric Differential Evolution (TDE) algorithm. The modification in TDE algorithm is made to further enhance its convergence speed. Further the Modified Trigonometric Differential Evolution (MTDE) algorithm is applied and evaluated for solving dynamic optimization problems encountered in chemical engineering. The performance of MTDE algorithm is compared with that of TDE and original DE algorithms. Results indicate that the MTDE algorithm is efficient and significantly faster than TDE and DE algorithms.