The purpose of this paper is to explain in detail the optimization of the sensitivity versus the power consumption of a pressure microsensor using multi-objective genetic algorithms.
The tradeoff between sensitivity and power consumption is analyzed and the Pareto frontier is identified by using NSGA-II, AMGA-II and ɛ-MOEA methods.
Comparison results demonstrate that NSGA-II provides optimal solutions over the entire design space for spread metric analysis, and AMGA-II is better for convergence metric analysis.
This paper provides a new multiobjective optimization tool for the designers of low power pressure microsensors.
This work was funded by the projects BATTLEWISE (TEC2011-29148-C02-01) of the SpanishMinistry of Economy and Competitiveness and MICROIN (SOLSUB–C200801000283) by the Canary Agency for Research, Innovation and Information Society.
Miguel Monzón-Verona, J., Garcia-Alonso, S., Sosa, J. and A. Montiel-Nelson, J. (2013), "Multi-objective genetic algorithms applied to low power pressure microsensor design", Engineering Computations, Vol. 30 No. 8, pp. 1128-1146. https://doi.org/10.1108/EC-03-2012-0072Download as .RIS
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