A Single-Objective Optimization of Water Quality Sensors in Water Distribution Networks Using Advanced Metaheuristic Techniques

This paper explores the intersection of water quality management and advanced metaheuristic algorithms (MAs) by optimizing the location of water quality sensors in urban water networks. A comparative analysis of ten cutting-edge MAs, Harris Hawk Optimization (HHO), Artemisinin Optimization (AO), Educational Competition Optimizer (ECO), Fata Morgana Algorithm (FATA), Moss Growth Optimization (MGO), Parrot Optimizer (PO), Polar Lights Optimizer (PLO), Rime Optimization Algorithm (RIME), Runge Kutta Optimization (RUN), and Weighted Mean of Vectors (INFO), was conducted to determine their effectiveness in minimizing the risk of contaminated water consumption. Both benchmark and real-world water network serve as case studies to assess algorithmic performance. The optimization process focuses on reducing the volume of contaminated water by treating sensor placement as a critical design variable. EPANET 2.2 software was integrated with the optimization algorithms to simulate water quality and hydraulic behavior within the networks. The obtained results from analysis of two urban water networks revealed that the newer algorithms, such as the RIME and FATA, exhibit superior convergence rates and stability compared to traditional methods. While all tested algorithms demonstrated satisfactory performance, this study provides foundational insights for future research, paving the way for more effective algorithmic solutions in water quality management.