In recent years, evolutionary algorithms (EAs) are gaining popularity for finding the optimal solution of non-linear multimodal problems encountered in many engineering disciplines. Differential evolution (DE), one of the evolutionary algorithms, is a novel optimization method capable of handling nondifferentiable, non-linear and multimodal objective functions. Previous studies have shown that differential evolution is an efficient, effective and robust evolutionary optimization method. Still, DE takes large computational time for optimizing the computationally expensive objective functions. And therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification to original DE that enhances the convergence rate without compromising on solution quality. Our modified differential evolution (MDE) algorithm utilizes only one set of population as against two sets in original DE at any given point of time in a generation. Such an improvement reduces the memory and computational efforts. The proposed MDE is applied to benchmark test functions and five non-linear chemical engineering problems. Results obtained are compared with those obtained using DE by considering the convergence history (CPU time and the number of runs converged to global optimum) and the established statistical techniques, taking into account the variability in the results, such as t-test. As compared to DE, MDE is found to perform better in locating the global optimal solution for all the problems considered. (c) 2006 Elsevier Ltd. All rights reserved.