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The optimal location of sensors involves the selection of type, number and location of sensors from a set of available instruments with certain values of cost, precision and reliability. The optimal design not only satisfies economic criteria, but also some requirements on the quality of key variable estimates. In this work, a methodology for solving the sensor network design problem based on Estimation of Distribution Algorithms is presented. These algorithms are included in the Evolutionary Computation paradigm and substitute the probability distribution estimation of a population composed by potential solutions and its subsequent sampling for the use of the classic crossover and mutation operators. The performance of the new strategy is evaluated and compared with that provided by other evolutionary techniques for the case of a steam metering network of a methanol synthesis plant.

Estimation of Distribution Algorithms: Applications to the Design of Process Sensor Networks