Reducing the Complexity of Multiobjective Water Distribution System Optimization Through Global Sensitivity Analysis


This study investigates the use of global sensitivity analysis as a screening tool to reduce the computational demands associated with multiobjective design and rehabilitation of water distribution systems (WDS). Sobol's method is used to screen insensitive decision variables and guide the formulation of reduced complexity WDS optimization problems (i.e., fewer decision variables). This sensitivity-informed problem decomposition dramatically reduces the computational demands associated with attaining high-quality approximations for optimal WDS trade-offs. This study demonstrates that the results for the reduced-complexity WDS problems can then be used to precondition and significantly enhance full search of the original WDS problem. Two case studies of increasing complexity-the New York Tunnels network and the Anytown network-are used to demonstrate the proposed methodology. In both cases, sensitivity analysis results reveal that WDS performance is strongly controlled by a small proportion of decision variables, which should be the focus of preconditioning problem formulations. Sensitivity-informed problem decomposition and preconditioning are evaluated rigorously for their ability to improve the efficiency, reliability, and effectiveness of multiobjective evolutionary optimization. Overall, this study reveals for the first time that the use of global sensitivity analysis is computationally efficient and potentially critical when solving the complex multiobjective WDS problems.