Stochastic Search Algorithms for Optimal Monitoring Network Designs

The design of efficient monitoring networks is very important to obtain a better understanding of environmental, ecological and epidemiological processes. In this paper we develop for the optimal design of monitoring networks a new hybrid genetic algorithm (HGA) which combines the standard genetic algorithm (GA) with a local search operator. We compare the performance of our HGA with two other stochastic search algorithms, a simulated annealing algorithm (SA) and a standard genetic algorithm (GA). Specifically, we consider the reduction of pre-existing largescale monitoring networks, when the optimality criterion is the maximization of the entropy of the included stations. The algorithms were tested on a set of simulated problems of different sizes, as well as on a real application involving the downsize of a large-scale environmental monitoring network. Even though the HGA spent more CPU time, it outperformed the other two algorithms in terms of solution quality for all the cases considered in the simulations as well as in the real application.