Multi-objective Immune Algorithm with Preference-Based Selection for Reservoir Flood Control Operation

Abstract

In reservoir flood control operation, the safety of upstream and downstream of the dam are the main two optimization goals with conflicts. In addition, the irrigation water demands is also an important issue considered by decision makers. Therefore, the dispatching schemes that meet the final water level constraint are preferred. Considering such preference in decision making, a novel preference-based selection operator is developed and combined with immune inspired optimization technique to form the proposed multi-objective immune algorithm with preference- based selection (MOIA-PS) for reservoir flood control operation. The unique of MOIA-PS is that it intends to obtain a set of preferred Pareto optimal solutions that located within a part of preferred area on the Pareto front rather than to find a good approximation of the entire Pareto front as most existing methods did. Experimental results on four typical floods at the Ankang reservoir have indicated that the preferred non-dominated solutions are distributed within a local area of preferred PF region. And the newly designed preference-based selection operator can guide the search of MOIA-PS towards the preferred PF region. Comparing with the outstanding multi-objective evolutionary algorithm NSGAII and the immune inspired multi-objective optimization algorithm NNIA, the proposed MOIA-PS obtains more non-dominated solutions that densely and evenly scattered within the preferred area of the Pareto front. MOIA-PS can find finding dispatching schemes that not only reduce the flood peak significantly and guarantee the dam safety well but also satisfy the irrigation water demands. It is a more efficient use of the computing efforts.