Energy which is the largest expense for water utility industries is greatly associated with the pump operation. An optimum pump operation schedule may reduce the cost significantly while maintaining the hydraulics in an acceptable range. To date evolutionary optimization algorithms are linked with the hydraulic simulation model to obtain the optimum pump operation schedule. However, this process of finding optimum solution requires extensive time consuming simulations. Therefore, computational time is the main constraint to develop a real time optimum controller for the pump operation. To reduce the computational time researchers are developing different strategies. This paper examines few strategies to develop the real time optimum controller. To reduce the computational time, optimization algorithm can start from a warm solution. Warm solution can be defined as the most recent solution which can be applicable for the time period for which the solution is being sought. Also, the relationships among pump operation energy, flow, demands and tank water levels can be linearized to get a linear program (LP) solved quickly. Metamodeling approach also can be used to approximate the nonlinear function. In this case, support vector machine (SVM), a type of artificial neural networks (ANNs) is trained with the expensive simulation data and then the model is linked with the evolutionary optimization algorithm to optimize the pump schedule. Results obtained by different approaches presented here are compared with results that obtained by a conventional hydraulic simulation model, EPANET linked with the evolutionary optimization algorithm, SFLA. Results are comparable to each other while the approaches proposed in this paper can find the near-optimal solutions with significantly minimum time.