NSGA-II parameterization for the optimal pressure sensor location in water distribution networks

Abstract

The optimal location of pressure sensors is typicallysolved using heuristic algorithms. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is one of the most used algorithms in the water industry, requiring a preliminary parameter tuning process. The lack of guidelines on how to tune model parameters generally limits the use of these algorithms by researchers or practitioners and, as such, fails to be used in real-life problems. The current paper explores different NSGA-II parameterizations for the optimal location of pressure sensors by using a multi-objective optimization methodology applied to a real distribution network. Results show that (i) both the uniform and simulated binary crossover operators (depending on the internal parameters) produce the best results, being the former recommended since it does not require further parameter tuning; (ii) polynomial mutation with lower probability value should be chosen; and (iii) the distribution indices of polynomial mutation have a minor effect on NSGA-II performance.