Differential evolution (DE) algorithm has shown good performance in many optimization problems. However, its control parameters greatly affect its performance and require many trials to determine the optimum values of control parameters for each specific optimization problem. In this paper, we present a self-adaptive DE with a new adaptation technique to improve the solution quality as well as increase the speed of convergence with a reduction in the computational cost. The proposed approach, called modified self-adaptive differential evolution (MSaDE), employs a new success-rate indicator of the strategies used to generate the trial vectors in conventional self-adaptive differential evolution (SaDE) algorithm. The proposed method has been tested on 22 benchmark problems and on the expanded solvent steam-assisted gravity drainage (ES-SAGD) recovery process. The results show that a significant speed-up is achieved in the exploitation and exploration capabilities of the self-adaptive DE algorithm. (C) 2018 Elsevier Ltd. All rights reserved.