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

Abstract

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.