In this paper, the model-based real-time optimization (RTO) is viewed as a kind of nonlinear parametric optimization problem which is solved repeatedly when parameter Values change. A novel RTO strategy-mnemonic enhancement optimization (MEO)-is proposed. The method preserves the past optimal solutions and corresponding, parameter values as experience and approximates the optimum based on the experience. The approximation is used by the optimization algorithm as a starting point to find the real optimum. The optimum is proved to be a continuous function of the parameter. This ensures that the distance between the optimum and the initial point tends to decrease as RTO continues to run. Thus MEO can improve the performance of RTO continually. Numerical experiments illustrate the continuity of the optimal set mapping, and the MEO method is compared with the traditional method. The results show that MEO outperforms the traditional method concerning the Solution time, the number of iterations, and the percentage of successful optimizations.