Reservoir flood control operation using multi-objective evolutionary algorithm with decomposition and preferences

Abstract

In this paper we propose a preference-based multi-objective optimization model for reservoir flood control operation (RFCO). This model takes the water preserving demand into consideration while optimizing two conflicting flood control objectives. A preference based multi-objective evolutionary algorithm with decomposition, named MOEA/D-PWA, is developed for solving the proposed RFCO model. For RFCO, it is challenging to define the preferred region formally, as the preference information is implicit and difficult to formulate. MOEA/D-PWA estimates the preferred region dynamically according to the final water level of solutions in the population, and then guides the search by propelling solutions towards the preferred region. Experimental results on four types of floods at the Ankang reservoir have illustrated that the suggested MOEA/D-PWA can successfully produce solutions in the preferred region of the Pareto front. The schedules obtained by MOEA/D-PWA can significantly reduce the flood peak and guarantee the dam safety as well. The proposed MOEA/D-PWA is also efficient in term of computational cost.