Research Article The Application of Multiobjective Genetic Algorithm to the Parameter Optimization of Single-Well Potential Stochastic Resonance Algorithm Aimed at Simultaneous Determination of Multiple Weak Chromatographic Peaks Haishan Deng, 1 Shaofei Xie, 2 Bingren Xiang, 3 Ying Zhan, 4 Wei Li, 1 Xiaohua Li, 5 Caiyun Jiang, 5 Xiaohong Wu, 5 and Dan Liu 1 1 Department of Pharmacy, College of Pharmacy, Nanjing University of Chinese Medicine, No. 138 Xianlin Avenue, Nanjing 210023, China 2 Nanjing Changao Pharmaceutical Technology Limited, No. 1 Hengfei Road, Economic and Technological Development Zone, Nanjing 210038, China 3 Center for Instrumental Analysis, China Pharmaceutical University, No. 24 Tongjiaxiang, Nanjing 210009, China 4 Zhongda Hospital Ailiated to Southeast University, Nanjing 210009, China 5 Department of Engineering and Technology, Jiangsu Institute of Economic and Trade Technology, Nanjing 210007, China Correspondence should be addressed to Haishan Deng; hs deng@njutcm.edu.cn and Shaofei Xie; cpuxsf@163.com Received 21 August 2013; Accepted 20 October 2013; Published 12 January 2014 Academic Editors: M. C. Yebra-Biurrun and R. Zakrzewski Copyright © 2014 Haishan Deng et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simpliied the process signiicantly. Even worse, it is oten diicult to keep ampliied peaks with beautiful peak shape. herefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e., S/N and peak shape) and multiple chromatographic peaks. he applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between diferent concentrations and responses. 1. Introduction Stochastic resonance algorithm (SRA) [1] is established based on a counterintuitive phenomenon that the signal- to-noise ratio (/) of a weak signal can be ampliied signiicantly in a nonlinear system by making the best of noise instead of iltering it [2]. he algorithm presents the unique advantages for superior detection of useful signal that submerged in heavy noise and provides an entirely new way for the detection of weak chromatographic peaks [3]. It has been successfully applied to many difer- ent ields of analytical chemistry, such as pharmaceutical analysis [4], food analysis, [5] and environmental analysis [6]. A nonlinear system is one of the necessary elements of SRA, and the one that is most frequently employed is a bistable system described as double-well potential with two system parameters. he optimization of the system param- eters is essential for the application of SRA. he initial goal of the optimization is to pursue the maximal signal-to-noise ratio (/) of the output peak, and, usually, the process of optimization is to search the optimal value one by one within a given range [7]. However, the ill-looking peak shape oten annoys the researchers. herefore, some improved algorithms with two or more parameters were developed to give attention to both the / and the chromatographic peak shape [8, 9]; of course, the workload of parameter optimization inevitably increased. In order to simplify the parameter optimization, Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 767018, 6 pages http://dx.doi.org/10.1155/2014/767018