改进鲸鱼优化算法及其在渣油加氢参数优化的应用

doi:10.11949/j.issn.0438-1157.20171128

化工学报 ›› 2018, Vol. 69 ›› Issue (3): 891-899.DOI: 10.11949/j.issn.0438-1157.20171128

改进鲸鱼优化算法及其在渣油加氢参数优化的应用

许瑜飞, 钱锋, 杨明磊, 杜文莉, 钟伟民

华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237

收稿日期:2017-08-18 修回日期:2017-08-22 出版日期:2018-03-05 发布日期:2018-03-05
通讯作者: 钱锋
基金资助:
国家科技支撑计划项目（2015BAF22B02）；国家自然科学基金项目（61422303，61590922）；中央高校基本科研业务费专项资金。

Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters

XU Yufei, QIAN Feng, YANG Minglei, DU Wenli, ZHONG Weimin

Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2017-08-18 Revised:2017-08-22 Online:2018-03-05 Published:2018-03-05
Supported by:
supported by the Project of National Research Program of China (2015BAF22B02), the National Natural Science Foundation of China (61422303, 61590922) and the Fundamental Research Funds for the Central Universities.

摘要/Abstract

摘要：

针对智能优化算法在处理非线性优化问题中存在的容易陷入局部最优和收敛精度差等问题，提出了一种基于结合差分进化和精英反向学习的改进鲸鱼算法（DEOBWOA）。该算法引入对立搜索初始化、精英反向学习，并结合差分进化进行变异修正，显著有效地提高WOA算法的收敛精度和收敛速度，提高其跳出局部最优的能力。之后采用8个标准测试函数进行仿真实验，结果表明：DEOBWOA算法与标准WOA、HCLPSO、DE算法相比，全局搜索能力和收敛速度都有较大提升。最后建立了渣油加氢动力学模型，考虑到渣油加氢过程中存在诸多典型的非线性约束问题，以某炼化厂渣油加氢装置为例，应用DEOBWOA对渣油加氢反应动力学模型参数进行优化，结果表明该算法能较好地处理实际工程优化问题。

关键词: 算法, 鲸鱼优化算法, 渣油加氢, 动力学模型, 参数估值, 优化

Abstract:

An improved whale algorithm (DEOBWOA) based on differential evolution and elite opposition-based learning is proposed to solve the problem that the intelligent optimization algorithm is easy to fall into the local optimum and the convergence precision in dealing with the nonlinear optimization problem is poor. The algorithm uses the opposing search initialization, elite opposition-based learning and combines with differential evolution, which can improve the convergence precision and convergence speed of the whale optimization (WOA) algorithm effectively and improve the ability to jump out of local optimum. 8 standard test functions are used to do simulation experiment. The results show that DEOBWOA algorithm has a better performance than WOA, heterogeneous comprehensive learning particle swarm optimization (HCLPSO) and differential evolution (DE). Finally, the kinetic model of residue hydrogenation was established, but there are many typical nonlinear constraints in the process of residue hydrogenation. So DEOBWOA was used to optimize the kinetic model parameters of residue hydrogenation in a refinery residue, which indicates the algorithm can deal with the practical engineering optimization problem.

Key words: algorithm, whale optimization algorithm, residue hydrogenation, kinetic modeling, parameter estimation, optimization

中图分类号:

TE624

许瑜飞, 钱锋, 杨明磊, 杜文莉, 钟伟民. 改进鲸鱼优化算法及其在渣油加氢参数优化的应用[J]. 化工学报, 2018, 69(3): 891-899.

XU Yufei, QIAN Feng, YANG Minglei, DU Wenli, ZHONG Weimin. Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters[J]. CIESC Journal, 2018, 69(3): 891-899.

参考文献 31

[1]	SUN Y, QI G, WANG Z, et al. Chaotic particle swarm optimization[C]//Proceedings of the 1999 Congress on Evolutionary Computation, IEEE. 2002:320-324.
[2]	QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.
[3]	WANG Y, CAI Z X, ZHANG Q F. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1):55-66.
[4]	DORIGO M, GAMBARDELLA L M. Ant colony system:a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1):53-66.
[5]	ELLABIB I, CALAMAI P, BASIR O. Exchange strategies for multiple ant colony system[J]. Information Sciences, 2007, 177(5):1248-1264.
[6]	KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization:artificial bee colony (ABC) algorithm[J]. Journal of Global Optimization, 2007, 39(3):459-471.
[7]	AKAY B, KARABOGA D. A modified artificial bee colony algorithm for real-parameter optimization[J]. Information Sciences, 2012, 192(1):120-142.
[8]	WU B, QIAN C H. Differential artificial bee colony algorithm for global numerical optimization[J]. Journal of Computers, 2011, 6(5):841-848.
[9]	LI X, LUO J, CHEN M R, et al. An improved shuffled frog-leaping algorithm with extremal optimization for continuous optimization[J]. Information Sciences, 2012, 192(6):143-151.
[10]	NIKNAM T, NARIMANI M R, JABBARI M, et al. A modified shuffle frog leaping algorithm for multi-objective optimal power flow[J]. Energy, 2011, 36(11):6420-6432.
[11]	LIN X, KE S, LI Z, et al. A fault diagnosis method of power systems based on improved objective function and genetic algorithm-Tabu search[J]. IEEE Transactions on Power Delivery, 2010, 25(3):1268-1274.
[12]	PAN J L, YE X H, XUE Q. A new method for sequential fault diagnosis based on ant algorithm[C]//Computational Intelligence and Design, 2009. ISCID'09. Second International Symposium on. DOI:10.1109/ISCID.2009.18.
[13]	KANG M, KIM J, KIM J M. Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm[J]. Information Sciences, 2015, 294(C):423-438.
[14]	ZHAO J H, WANG N. A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling[J]. Computers & Chemical Engineering, 2011, 35(2):272-283.
[15]	CHEN C, YANG B L, YUAN J, et al. Establishment and solution of eight-lump kinetic model for FCC gasoline secondary reaction using particle swarm optimization[J]. Fuel, 2007, 86(15):2325-2332.
[16]	CUADROS J F, MELO D C, FILHO R M, et al. Fluid catalytic cracking optimization using factorial design and genetic algorithm techniques[J]. Canadian Journal of Chemical Engineering, 2013, 91(2):279-290.
[17]	栗伟, 苏宏业, 刘瑞兰. 粒子群优化算法在催化裂化模型参数估计中的应用[J]. 化工学报, 2010, 61(8):1927-1932. LI W, SU H Y, LIU R L. Parameter estimation of catalytic cracking model using PSO algorithm[J]. CIESC Journal, 2010, 61(8):1927-1932.
[18]	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95:51-67.
[19]	牛培峰, 吴志良, 马云鹏, 等. 基于鲸鱼优化算法的汽轮机热耗率模型预测[J]. 化工学报, 2017, 68(3):1049-1057. NIU P F, WU Z L, MA Y P, et al. Prediction of steam turbine heat consumption rate based on whale[J]. CIESC Journal, 2017, 68(3):1049-1057.
[20]	刘竹松, 李生. 正余混沌双弦鲸鱼优化算法[J]. 计算机工程与应用, 2017, DOI:10.3778/j.issn./002-8331.1610-0395. LIU Z S, LI S. Whale optimization algorithm based on chaotic sine cosine operator[J]. Computer Engineering and Applications, 2017, DOI:10.3778/j.issn./002-8331.1610-0395.
[21]	LYNN N, SUGANTHAN P N. Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation[J]. Swarm & Evolutionary Computation, 2015, 24:11-24.
[22]	TIZHOOSH H. Opposition-based learn:a new scheme for machine intelligence[C]//Proceedings of the International Conference on Computational Intelligence for Modeling Control and Automation. Vienna, Austria:IEEE, 2005:695-701.
[23]	董明刚, 牛秦洲, 杨祥. 基于对立策略的螺栓遗传算法[J]. 计算机工程, 2009, 35(20):239-241. DONG M G, NIU Q Z, YANG X. Opposition-based stud genetic algorithm[J]. Computer Engineering, 2009, 35(20):239-241.
[24]	RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1):64-79.
[25]	WANG H, LI H, LIU Y, et al. Opposition-based particle swarm algorithm with Cauchy mutation[C]//Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. IEEE, 2007:4750-4756.
[26]	WANG H, WU Z, RAHNAMAYAN S, et al. Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences, 2011, 181(20):4699-4714.
[27]	周新宇, 吴志健, 王晖, 等. 一种精英反向学习的粒子群优化算法[J]. 电子学报, 2013, 41(8):1647-1652. ZHOU X Y, WU Z J, WANG H, et al. Elite opposition-based particle swarm optimization[J]. Acta Electronica Sinica, 2013, 41(8):1647-1652.
[28]	LYNN N, SUGANTHAN P N. Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation[J]. Swarm & Evolutionary Computation, 2015, 24:11-24.
[29]	韩崇仁, 陶宗乾, 王建平. 含硫渣油加工路线选择的探讨[J]. 当代石油石化, 2003, 11(7):14-19. HAN C R, TAO Z Q, WANG J P. Probing the choices of sulfur-containing residuum processing lines[J]. Petrochemical Industry Trends, 2003, 11(7):14-19.
[30]	RUEDAVELÁSQUEZ R I, FREUND H, QIAN K, et al. Characterization of asphaltene building blocks by cracking under favorable hydrogenation conditions[J]. Energy & Fuels, 2013, 27(4):1817-1829.
[31]	MENOUFY M F, AHMED H S, BETIHA M A, et al. A comparative study on hydrocracking and hydrovisbreaking combination for heavy vacuum residue conversion[J]. Fuel, 2014, 119(1):106-110.

改进鲸鱼优化算法及其在渣油加氢参数优化的应用

Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters

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