Multi-objective genetic algorithms applied to low power pressure microsensor design.

Purpose – 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. Design/methodology/approach – The tradeoff between sensitivity and power consumption is analyzed and the Pareto frontier is identified by using NSGA-II, AMGA-II and ε-MOEA methods. Findings – 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. Originality/value – This paper provides a new multiobjective optimization tool for the designers of low power pressure microsensors. [ABSTRACT FROM AUTHOR]