Well designed monitoring networks are essential for the effective management of groundwater resources but the costs of monitoring well installations and sampling can prove prohibitive. The challenge is to obtain adequate water quality and quantity information with a minimum number of wells and sampling points, a task that can be approached objectively and effectively using numerical optimization methods. One recently developed optimization approach involves particle swarm optimization (PSO), a population based stochastic optimization technique that was inspired by the social behavior observed in bird flocks and schools of fish. The system is first initialized with a population of randomly generated particles (i.e. candidate solutions); thereafter, searches for optima are conducted iteratively. However, unlike genetic algorithms, PSO has no evolutionary operators (e.g. crossover and mutation) and instead, potential solutions “fly” through the problem space by following the current optimum particles. As a case study, the particle swarm algorithm technique was used to optimize an existing network of 57 monitoring wells located in the Astaneh aquifer in the north of Iran. The traveling salesperson problem (TSP) analogy was used to initialize the problem and PSO was used to provide the optimal solution.
