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

Abstract

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 1-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.

Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

“…In general, the precision is better than binary codification. It can be improved by adding more bits, but this increases the simulation time [ 19 , 21 ]. As the variable of the parameter space of an optimization problem is continuous, a real coded GA is possibly preferred [ 41 ].…”

“…The execution time was approximately 56 min for 100 generations. In this problem, each individual of the population was equivalent to a chromosome, which was constituted by the union of the physical properties to be determined, and [ 21 ].…”

“…In [ 20 ], a GA was used with the finite element method for the design of transformers. In [ 21 ], a new multi-objective optimization tool was provided for the design of low-power-pressure microsensors using NSGA-II.…”