Performance of modified differential evolution for optimal design of complex and non-linear chemical processes

Abstract

Differential evolution (DE) is an evolutionary optimization technique that is exceptionally simple, fast, and robust at numerical optimization. However, the convergence rate of DE in optimizing a computationally expensive objective function still does not meet our requirements, and an attempt to speed up DE is considered necessary. This article introduces a modified differential evolution (MDE) that enhances the convergence rate without compromising the robustness. MDE algorithm utilizes only one set of population array as against two sets in original DE at any given generation. This modification improves the convergence rate of DE and at the same time maintains the robustness. The performance of MDE is evaluated on two benchmark test functions followed by nonlinear chemical processes. The simulation results show empirical evidences on the efficiency and effectiveness of the proposed MDE.