[1] Zhou XiaShen Jiong, Shen JianxianLi Yiguo,. New immune multiobjective optimization algorithmand its application in boiler combustion optimization [J]. Journal of Southeast University (English Edition), 2010, 26 (4): 563-568. [doi:10.3969/j.issn.1003-7985.2010.04.013]
Copy
New immune multiobjective optimization algorithmand its application in boiler combustion optimization(
)
一种新的免疫多目标优化算法及其在锅炉燃烧优化中的应用
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 26
- Issue:
- 2010 4
- Page:
- 563-568
- Research Field:
- Energy and Power Engineering
- Publishing date:
- 2010-12-30
Info
- Title:
- New immune multiobjective optimization algorithmand its application in boiler combustion optimization
- 一种新的免疫多目标优化算法及其在锅炉燃烧优化中的应用
- Author(s):
- Zhou XiaShen Jiong; Shen JianxianLi Yiguo
- School of Energy and Environment, Southeast University, Nanjing 210096, China
- PACS:
- TK227.1
- DOI:
- 10.3969/j.issn.1003-7985.2010.04.013
- Abstract:
- In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously, an immune cell subsets based multiobjective optimization algorithm(ICSMOA)is proposed. In the ICSMOA, the subset division operator and the immunological tolerance operation are defined. Preference can be easily addressed by using the subset division operator, and the distribution of the solutions can be guaranteed by the immunological tolerance operation. Using the ICSMOA, a group of Pareto optimal solutions can be obtained. However, by the traditional weighting method(WM), only one solution can be obtained and it cannot be judged as Pareto optimal or not. In contrast to the solutions obtained by the repeatedly performed WM, the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM. In addition, the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms. More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.
- 为了综合考虑锅炉燃烧优化问题中锅炉效率与NOxx排放2个目标, 提出了一种新的基于免疫细胞亚群的多目标优化算法ICSMOA.算法定义了亚群划分算子与免疫耐受算子, 亚群划分可以很方便地表达偏好, 免疫耐受则能保证解的分布性.ICSMOA的运行结果为一组Pareto最优解, 而传统的加权法的运行结果为一个不能判断Pareto占优与否的解.与多次运行加权法获得的结果相比, 所提算法的运行结果优于加权法.另外, 运行ICSMOA所获得的Pareto前沿不同于经典的多目标优化算法, 它可以输出更多的满足决策者偏好的解, 从而更适合于工业应用.
References:
[1] Xu Chang, Lü Jianhong, Zheng Yuan, et al. A boiler combustion global optimization on efficiency and low NOxx emission object[J]. Proceedings of the CSEE, 2006, 26(4):46-50.(in Chinese)
[2] Wang Peihong, Li Leilei, Chen Qiang, et al. Research on applications of artificial intelligence to combustion optimization in a coal-fired boiler[J]. Proceedings of the CSEE, 2004, 24(4):184-188.(in Chinese)
[3] Chen Minsheng, Liu Dingping. Multiobjective optimization of coal-fired boiler combustion based on LSSVM and SPEA2[J]. East China Electric Power, 2006, 34(3):50-53.(in Chinese)
[4] Wu Feng, Zhou Hao, Zheng Ligang, et al. Multi-objective optimization of coal-fired boiler combustion based on nondominated sorting genetic algorithm[J]. Proceedings of the CSEE, 2009, 29(29):7-12.(in Chinese)
[5] Hunt J E, Cooke D E. Learning using an artificial immune system[J]. Journal of Network and Computer Application, 1996, 19(4):189-212.
[6] Smith D J, Forrest S, Perelson A S. Immunological memory is associative[C]//International Conference on Multiagent Systems. Kyoto, Japan, 1996: 62-70.
[7] De Castro L N, Von Zuben F J. Learning and optimization using the clonal selection principle[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(3):239-251.
[8] Kim J, Bentley P. Negative selection and niching by an artificial immune system for network instrusion detection[C]//1999 Genetic and Evolutionary Computation Conference. Orlando, FL, USA, 1999:149-158.
[9] Tang Z, Yamaguchi T, Tashima K, et al. Multiple-valued immune network model and its simulations[C]//Proceedings of the 27th International Symposium on Multiple-Valued Logic. Antigonish, Nova Scotia, Canada, 1997:519-524.
[10] Zhou Xia, Shen Jiong, Shen Jianxian. An immune recognition based algorithm for finding non-dominated set in multiobjective optimization[C]//IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application. Wuhan, China, 2008: 305-310.
[11] Coello C A C, Cortés N C. Solving multiobjective optimization problems using an artificial immune system[J]. Genetic Programming and Evolvable Machines, 2005, 6(2):163-190.
[12] Gong Feili. Medical immunology[M]. Beijing: Science Press, 2003:1-6.(in Chinese)
[13] Li Tao. Computer immunolgy[M]. Beijing: Publishing House of Electronics Industry, 2004:43-48.(in Chinese)
[14] Nussenzweig M C. Immune receptor editing: revise and select[J]. Cell, 1998, 95(7):875-878.
[15] Vapnik V N. The nature of statistical learning theory [M].2nd ed. New York: Springer, 2000:181-224.
[16] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3):293-300.
[17] Xu Chang, Lü Jianhong, Zheng Yuan, et al. An experiment and analysis for a boiler combustion optimization on efficiency and NOxx emission[J]. Boiler Technology, 2006, 37(5):69-74.(in Chinese)
[18] Keerthi S S, Lin C J.Asymptotic behaviors of support vector machines with Gaussian kernel[J].Neural Computation, 2003, 15(7):1667-1689.
[19] Shang Wenfeng, Zhao Shengdun, Shen Yajing. Application of LSSVM optimized by genetic algortihm to modeling of switched reluctance motor[J]. Proceedings of the CSEE, 2009, 29(12): 65-69.(in Chinese)
Memo
- Memo:
-
Biographies: Zhou Xia(1976—), female, graduate; Shen Jiong(corresponding author), male, doctor, professor, shenj@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.51036002, 51076027), the Key Project of Ministry of Education of China(No.108060).
Citation: Zhou Xia, Shen Jiong, Shen Jianxian, et al. New immune multiobjective optimization algorithm and its application in boiler combustion optimization[J].Journal of Southeast University(English Edition), 2010, 26(4):563-568.