Modified hybrid particle swarm optimization: multivariate calibration of water supply networks

Abstract Calibration is essential to ensure the accuracy of hydraulic models, adjusting hydraulic parameters to reliably represent real systems. This work presents the implementation of the Modified Hybrid Particle Swarm Optimization with ‘fmincon’ (PSO-HM) evolutionary search method to propose efficient and robust solutions for large networks. PSO-HM was applied in multivariate and multiobjective calibration, estimating hydraulic parameters based on node pressure and pipeline flow rate, through minimization of the objective function. The methodology was validated with the calibration of the roughness and demand of the Water Distribution Pilot System (WDPS) at the Hydraulic Energy Efficiency Laboratory (LENHS) and the roughness of the C-Town Benchmark network. The calibration algorithm, focused on the local optimal search for roughness and demand, achieved satisfactory results in the dynamic calibration with error 0.016% for pressure and 0.1% for flow. The results demonstrate the computational efficiency and robustness of PSO-HM compared to the genetic algorithm, calibrating hydraulic parameters with low computational cost, even for large supply networks.