Pressure management and minimizing leakage toward sustainability in a water distribution system utilizing a metaheuristic approach

ABSTRACT Pressure management and optimal placement of pressure reducing valves (PRVs) play a critical role in minimizing leakage and maintaining operational efficiency in water distribution networks (WDNs). While evolutionary and swarm-based algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have demonstrated promising results for PRV configuration, their efficiency is often contingent on careful parameter tuning and can be limited by convergence sensitivity or complex network scenarios. This study systematically evaluates the performance of the teaching-learning-based optimization (TLBO) algorithm – a population-based, parameter-free metaheuristic – for PRV placement and setting adjustment. Exploiting TLBO's teacher and learner phases, the approach achieves robust, repeatable leakage reduction without the need for intensive algorithm customization. Comparative results on a benchmark WDN confirm that TLBO not only minimizes the number of objective function evaluations but also consistently identifies PRV configurations that outperform or match those found by GA, PSO, and other established methods. The findings underline TLBO's practical advantages and highlight future opportunities to combine intelligent pipe selection strategies and real-time adaptive PRV control for further improvement in leakage management.