自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

doi:10.11949/j.issn.0438-1157.20141471

化工学报 ›› 2015, Vol. 66 ›› Issue (1): 222-227.DOI: 10.11949/j.issn.0438-1157.20141471

自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

徐文星, 何骞, 戴波, 张慧平

北京石油化工学院信息工程学院, 北京 102617

收稿日期:2014-09-28 修回日期:2014-10-14 出版日期:2015-01-05 发布日期:2015-01-05
通讯作者: 戴波
基金资助:
国家自然科学基金项目(61304217);北京市优秀人才培养资助项目(2013D005005000005);北京市自然科学基金项目(4142039)。

Adaptive hybrid particle swarm optimizer for constrained optimization problems and its application in parameter estimation of soft sensing

XU Wenxing, HE Qian, DAI Bo, ZHANG Huiping

College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China

Received:2014-09-28 Revised:2014-10-14 Online:2015-01-05 Published:2015-01-05
Supported by:
supported by the National Natural Science Foundation of China (61304217).

摘要/Abstract

摘要：

对于软测量模型参数估计问题, 针对传统梯度法求解非线性最小二乘模型时依赖初值、需要追加趋势分析进行验证和无法直接求解复杂问题的缺陷, 提出将参数估计化为约束优化问题, 使用混合优化算法求解的新思路。为此提出一种自适应混合粒子群约束优化算法(AHPSO-C)。在AHPSO-C算法中, 为平衡全局搜索(混沌粒子群)和局部搜索(内点法), 引入自适应内点法最大函数评价次数更新策略。对12个经典测试函数的仿真结果表明, AHPSO-C是求解约束优化问题的一种有效算法。将算法用于淤浆法高密度聚乙烯(HDPE)串级反应过程中熔融指数软测量模型参数估计, 验证了方法的可行性与优越性。

关键词: 软测量, 参数估计, 约束优化, 粒子群, 自适应

Abstract:

Tranditionally the problems of parameter estimation of soft sensing model are solved by using the traditional gradient methods to optimize the nonlinear least squares models. However, there exist the disadvantages of the dependence on the initial solution, the requirement of additional trend analysis to verify model correctness and the insufficiency in solving complex problems. Therefore, a new idea of transforming the original parameter estimation problem into a constrained optimization problem and using a hybrid optimization algorithm to solve it is developed. An adaptive hybrid particle swarm optimization algorithm for constrained optimization problems (AHPSO-C) is proposed. In AHPSO-C, an update strategy of maximum function evaluations for the interior-point method (IPM) is imported to balance the global search (chaotic particle swarm optimization) and local search (IPM). The simulation results for the 12 classic benchmark functions indicate that AHPSO-C is an effective algorithm for solving constrained optimization problems. The feasibility and superiority of the method is illustrated with the challenging parameter estimation of soft sensing model for melt index in a cascade slurry HDPE reaction process.

Key words: soft sensing, parameter estimation, constrained optimization, particle swarm optimization, self-adaptive

中图分类号:

徐文星, 何骞, 戴波, 张慧平. 自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用[J]. 化工学报, 2015, 66(1): 222-227.

XU Wenxing, HE Qian, DAI Bo, ZHANG Huiping. Adaptive hybrid particle swarm optimizer for constrained optimization problems and its application in parameter estimation of soft sensing[J]. CIESC Journal, 2015, 66(1): 222-227.

参考文献 22

[1]	Cao Pengfei (曹鹏飞), Luo Xionglin (罗雄麟). Modeling of soft sensor for chemical process [J]. CIESC Journal (化工学报), 2013, 64 (3): 788-800
[2]	McAuley K B, MacGregor J F. On-line inference of polymer properties in an industrial polyethylene reactor [J]. AIChE J., 1991, 37: 825-835
[3]	Adilson J A, Ruberts M F. Soft sensors development for online bioreactor state estimation [J]. Comput. Chem. Eng., 2000, 24 (7): 1099-1103
[4]	Zhang Y. Application of T-S fuzzy neural network based on declination compensation in soft sensing [J]. Math Comput. Simulat., 2012, 86: 92-99
[5]	Shao Weiming, Tian Xuemin, Wang Ping. Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division [J]. Chinese Journal of Chemical Engineering, 2014, 22 (7): 828-836
[6]	David Müller, Erik Esche, Diana C López C, Günter Wozny. An algorithm for the identification and estimation of relevant parameters for optimization under uncertainty [J]. Computers & Chemical Engineering, 2014, 71: 94-103
[7]	Gu Ya, Ding Feng, Li Junhong. States based iterative parameter estimation for a state space model with multi-state delays using decomposition [J]. Signal Processing, 2015, 106: 294-300
[8]	Wang Xin (王昕), Wang Zhenlei (王振雷). Multi-model soft sensor based on Dempster-Shafer rule [J]. Control Theory & Applications (控制理论与应用), 2014, 31 (5): 632-637
[9]	Qiao Zongliang (乔宗良), Zhang Lei (张蕾), Zhou Jianxin (周建新), Si Fengqi (司风琪), Xu Zhigao (徐治皋). Soft sensor modeling method based on improved CPSO-LSSVM and its applications [J]. Chinese Journal of Scientific Instrument (仪器仪表学报), 2014, 35 (1): 234-240
[10]	Abhisek Roy Chowdhary. Application of self-tuning high-fidelity dynamic simulation model for soft-sensing [J]. Computer Aided Chemical Engineering, 2014, 34: 687-692
[11]	Mordechai Shacham, Neima Brauner. Application of stepwise regression for dynamic parameter estimation [J]. Computers & Chemical Engineering, 2014, 69: 26-38
[12]	Wei Yujie (魏宇杰), Shang Chao (尚超), Gao Xinqing (高莘青), Fan Zhi (范志), Huang Dexian (黄德先). Dynamic process based soft sensing of melt flow rate [J]. CIESC Journal (化工学报), 2014, 65 (8): 3062-3070
[13]	Li Jiehua (李洁华), Zhu Qunxiong (朱群雄). The modeling of polymerization process for slurry high density polyethylene (HDPE) [J]. Computers and Applied Chemistry (计算机与应用化学), 2011, 28 (2): 181-186
[14]	Xu W X, Geng Z Q, Zhu Q X, Gu X B. A piecewise linear chaotic map and SQP based robust hybrid particle swarm algorithm [J]. Inform. Sciences, 2013, 218 (1): 85-102
[15]	Manoj T, Suraj S, Hemant J. A modified real coded genetic algorithm for constrained optimization [J]. Appl. Math. Comput., 2014, 235 (25): 292-317
[16]	Long W, Liang X M, Huang Y F, Chen Y X. A hybrid differential evolution augmented Lagrangian method for constrained numerical and engineering optimization [J]. Comput. Aided Design, 2013, 45 (12): 1562-1574
[17]	Zhu Q X, Wang Z Y, Xu W X, Geng Z Q. A constraints scattered memetic algorithm for constrained optimization problem [J]. Journal of Computers, 2012, 7 (11): 2788-2796
[18]	Liang J J, Suganthan P N. Coevolutionary comprehensive learning particle swarm optimizer//Proceedings of IEEE Congress on Evolutionary Computation [C]. Piscataway: IEEE Press, 2010: 1-8
[19]	Liu Yanmin (刘衍民). Hybrid particle swarm optimizer for constrained optimization problems [J]. J. Tsinghua Univ: Sci. & Tech. (清华大学学报: 自然科学版), 2013, 53 (2): 242-246
[20]	Xiang T, Liao X F, Wong K W. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map [J]. Appl. Math. Comput., 2007, 190 (2): 1637-1645
[21]	Waltz R A, Morales J L, Nocedal J, Orban D. An interior algorithm for nonlinear optimization that combines line search and trust region steps [J]. Math. Program, 2006, 107 (3): 391-408
[22]	Deb K, Gene A S. A robust optimal design technique for mechanical component design//Evolutionary Algorithms in Engineering Applications [M]. Berlin: Springer-Verlag, 1997: 497-514

自适应混合粒子群约束优化算法及其在软测量模型参数估计中的应用

Adaptive hybrid particle swarm optimizer for constrained optimization problems and its application in parameter estimation of soft sensing

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