Efficient resizing and topological optimization of real‐world water distribution networks in a multi‐criteria decision‐making framework

This study addresses complex multi‐objective optimization challenges in large‐scale, real‐world water distribution networks (WDNs). The primary objectives are to improve a water quality index (water age) and network resilience by optimizing pipe diameters and network topology as decision variables. The proposed approaches leverage the non‐dominated sorting genetic algorithm II (NSGA‐II) producing Pareto optimal alternatives for water utility decision‐makers. To enhance computational convergence runtime and solution quality of optimization, novel techniques are employed. These include advanced NSGA‐II constraint handling, search space reduction, graph theory‐based formulation of decision variables, constraints, and objective functions, as well as multi‐stage and hydraulic‐free optimization strategies. Furthermore, soft constraints are relaxed and integrated into Pareto decision‐making spaces to provide a comprehensive, multi‐criteria decision‐making framework. The approaches are applied to a real case study, and the results demonstrate optimization performance improvements, with efficiency increasing by approximately 20% (in terms of convergence speed). Additionally, water age is reduced by 52% while achieving favorable results in the hydraulic and topological criteria. These findings highlight the effectiveness of the proposed methodologies in addressing WDN optimization challenges.