Abstract
A multi-objective optimization method using genetic algorithm was proposed for sensor array optimization. Based on information theory, selectivity and diversity were used as the criteria for constructing two objective functions. A statistic measurement of resolving power, general resolution factor, and visual inspection were used to evaluate the optimization results with the aid of principal component analysis. In each Pareto set, most nondominated solutions had better statistics than the combination using all potential sensors. Also the principal component plots showed that different vapor classes were generally better separated after optimization. The experiment results indicated that the proposed method could successfully identify a set of Pareto optimal solutions of small size; and most optimized sensor arrays provided input with improved quality, i.e. better separation of target analytes. The running time for implementing the multi-objective optimization was satisfactory.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
