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Multi-objective optimization of sensor array using genetic algorithm
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DOI：10.1016/j.snb.2011.07.048.png)
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摘要
En 中文
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. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective optimization
Entropy
Information gain
Selectivity
Diversity
Genetic algorithms
Sensor array
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7.7
论文数:
3.4W
被引数:
13.6W
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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
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分享收藏Quantitative study of the resolving power of arrays of carbon black-polymer composites in various vapor-sensing tasks.png)
ANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
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