Interactive multiobjective evolutionary optimization model for dam management support

Dam management optimization is a complex multiobjective problem, whose current solutions struggle to spread in normal practice. Here, an interactive evolutionary multiobjective optimization method is proposed, which involves stakeholders by using multi-criteria decision analysis to embed a parsimonious elicitation of their preferences and expertise in the optimization process. Specifically: (i) one or more decision-makers are asked to rank a set of representative dam management strategies in order of preference; (ii) the ranking is used to build a preference model; and (iii) the preference model is used in an evolutionary multiobjective optimization algorithm to converge to the part of the Pareto front most preferred by the decision-makers. The inclusion of a user-friendly interaction in the optimization methodology makes the process more understandable for stakeholders and policy-makers, and it allows to address some of the key obstacles to the practical implementation of multiobjective dam management optimization, even when a large number of objectives is considered. The proposed methodology is applied to a case study (Lake Pozzillo, Sicily, Italy) with five objectives, where flood control is in contrast with irrigation water demand and hydropower production. Five simple and easy to implement optimal strategies are obtained by interacting with five decision-makers. The results are compared to the management practice currently used, indicating that the proposed approach can satisfy water demands while greatly improving flood attenuation. • Dam managers and stakeholders are included in the optimization process. • A user-friendly parsimonious elicitation of the objective’s trade-offs is used. • Users’ preference models are embedded in an evolutionary algorithm. • Flood control can reduce peak discharges by 70% without decreasing water supply.