Toward sustainable water quality monitoring systems using particle swarm, Ant Colony, and Tabu Search optimization methods

Considering the growing importance of sustainable development and value engineering in various fields, especially in water quality management, superior attention has been paid to reducing costs in water quality monitoring systems. One of the methods, which reduces environmental and economic costs in water quality monitoring networks, is to decrease the number of Water Quality Monitoring Stations (WQMS). In the present study, the optimization of the WQMS in the Mond River basin located in the south of the Fars province in Iran was studied. Three algorithms, including Tabu Search (TS), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), were applied to the existing WQMS to determine the optimal number of stations with the maximum number of station combinations for different applications. Then, we compared the performance of these three algorithms. The results presented that the number of existing WQMS can be reduced from 16 to 12 for irrigation and 16 to 11 for drinking purposes. As a result, the number of stations suggested for the Mond River basin was selected 12 out of 16 monitoring stations for both water usages. The results are consistent with previous studies that investigated the application of the Genetic Algorithm (GA) and Dynamic Programming Approach (DPA) for the optimization of WQMS in the Mond River.