A Clonal Selection Algorithm for the C-Town Network Calibration

This paper presents the calibration results of the C-Town network obtained using an artificial immune algorithm called Clonal Selection Algorithm (CLONALG).The calibration problem was formulated as determining the network model parameters such that the best match between measured and predicted data is obtained. The model parameters to be determined include pipe roughness values, valve closures, nodal demands, pump controls and valve settings. The artificial immune algorithm used in this work evolves a population of candidate solutions using the clonal selection principle. The main components of CLONALG include fitness based cloning and maturation of cloned population. The proposed model was applied to calibrate the C-Town network. The network calibration parameters obtained, when input into the C-Town hydraulic model, produced reasonably good match between the predicted and the observed tank water levels and pump station flows. Various performance measures were calculated and presented in the paper .