This study investigates intelligent optimization techniques for improving flow control in water distribution networks (WDNs). As urbanization, population growth, and climate change increase pressure on water systems, efficient management of water flow, energy consumption, and operational costs becomes essential. WDNs are complex dynamic systems characterized by nonlinear hydraulic behavior, time-varying demand, and operational constraints involving pumps, valves, and storage units. The paper reviews and integrates various optimization approaches, including heuristic and metaheuristic methods, mathematical programming, and machine learning techniques. These approaches aim to enhance network performance by optimizing water allocation, pressure regulation, demand forecasting, and energy efficiency. Hybrid frameworks combining optimization algorithms with real-time control and predictive models are highlighted as effective solutions for addressing system complexity. Furthermore, the study emphasizes the importance of hydraulic modeling, simulation tools, and sensor-based data acquisition for real-time monitoring and decision-making. Performance evaluation metrics such as energy efficiency, reliability, resilience, and water quality are also discussed. The integration of intelligent optimization with data-driven and predictive approaches is presented as a key strategy for achieving sustainable, cost-effective, and resilient water distribution systems under uncertainty.
Mahdar et al. (2026) studied this question.