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1 钱锋1 杨明磊1 杜文莉1 钟伟民1

(1.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)

【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;

【DOI】

【基金资助】 国家科技支撑计划项目(2015BAF22B02) supported by the Project of National Research Program of China(2015BAF22B02) 国家自然科学基金项目(61422303,61590922) the National Natural Science Foundation of China(61422303;61590922) 中央高校基本科研业务费专项资金 the Fundamental Research Funds for the Central Universities

Download this article

    References

    [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 lowspeed 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]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]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]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]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.Oppositionbased 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]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]HAN C R,TAO Z Q,WANG J P.Probing the choices of sulfurcontaining 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.

This Article

ISSN:0438-1157

CN: 11-1946/TQ

Vol 69, No. 03, Pages 891-899

March 2018

Downloads:0

Share
Article Outline

摘要

  • 引言
  • 1 改进鲸鱼优化算法
  • 2 仿真结果及分析
  • 3 渣油加氢过程参数优化应用
  • 4 结论
  • 参考文献