<address id="xrvvb"><listing id="xrvvb"></listing></address>

          <form id="xrvvb"></form>

          <address id="xrvvb"></address>

          華中科技大學學報(自然科學版) 2020, Vol. 48 Issue (9): 12-18 DOI10.13245/j.hust.200904

          欄目:計算機與控制工程
          基于組合三角變異差分進化算法的化工參數辨識
          熊小峰 a , 劉嘯嬋 a , 郭肇祿 a , b , 張文生 b
          a. 江西理工大學理學院,江西 贛州 341000
          b. 中國科學院自動化研究所,北京 100190
          摘要 針對傳統方法在解決化工參數辨識問題中易陷入局部最優、導致求解精度不足的問題,提出了一種組合三角變異差分進化(CTMDE)算法,融入了組合三角高斯變異策略和DE/current-to-pbest/1變異策略.其中,組合三角高斯變異策略引入了組合權重來適應性利用較優個體、一般個體、當前個體的信息,維持種群多樣性;而DE/current-to-pbest/1變異策略能夠利用種群中的較優個體來指導搜索,對解空間的開采能力較強.兩者結合使得算法在加快收斂速度的同時降低陷入局部最優的可能性.在12個基準測試函數上,將CTMDE算法與其他新近DE算法進行比較,并將CTMDE算法應用于甲醇轉化為烴類物質的參數辨識問題.實驗結果表明:CTMDE算法具有較好的尋優性能,且在化工參數辨識問題上具有較好的求解效果.
          關鍵詞 差分進化 ;高斯變異 ;局部最優 ;反應動力學 ;參數辨識
          Parameters identification of chemical reaction kinetics based on differential evolution algorithm with combined triangular mutation strategy
          XIONG Xiaofeng a , LIU Xiaochan a , GUO Zhaolu a , b , ZHANG Wensheng b
          a. School of Science,Jiangxi University of Science and Technology,Ganzhou 341000,Jiangxi China
          b. Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
          Abstract The traditional methods are prone to falling into local optimum when identifying the parameters of chemical reaction kinetics,which may obtain unsatisfactory results.To solve this problem,a differential evolution with combined triangular mutation (CTMDE) strategy was proposed.The combined triangular mutation strategy and the DE/current-to-pbest/1 mutation strategy were integrated in CTMDE to improve the performance.In the proposed CTMDE,the combined triangular mutation strategy introduced a combination weight to adaptively employ the information of the better individual,the general individual,and the current individual to maintain the population diversity.Meanwhile,the DE/current-to-pbest/1 mutation strategy can use the better individual to guide the search,and has a strong ability to exploit the solution space.The combination of the two mutation strategies can accelerate the convergence rate,which can also decrease the probability of falling into local optimum.In the experiments,CTMDE was compared with several excellent DE algorithms on 12 benchmark test functions,and it was applied to identify the parameters of the methanol-to-hydrocarbons.The experimental results show that CTMDE can achieve better performance and it is an effective approach for parameters identification of chemical reaction kinetics.
          Keywords differential evolution ; Gaussian mutation ; local optimum ; reaction kinetics ; parameter identification
          基金資助國家自然科學基金資助項目(61662029,U1636220);江西省教育廳科技項目(GJJ160623,GJJ170495);江西理工大學青年英才支持計劃資助項目(2018)

          中圖分類號TP301
          文獻標志碼A
          文章編號1671-4512(2020)09-0012-07
          參考文獻
          [1] 陳旭,徐斌,梅從立,等.基于二次插值教學優化的化工動態參數估計方法[J].華東理工大學學報(自然科學版),2017,43(6):824-828.
          [2] 吳擎,張春江,高亮.一種基于混合交叉的差分進化算法[J].華中科技大學學報(自然科學版),2018,46(5):78-83.
          [3] 柳長安,王曉鵬,劉春陽,等.基于改進灰狼優化算法的無人機三維航跡規劃[J].華中科技大學學報(自然科學版),2017,45(10):38-42.
          [4] 閤大海,李元香,劉偉.求解約束優化問題的加速CMODE算法[J].華中科技大學學報(自然科學版),2016,44(4):48-52.
          [5] STORN R,PRICE K.Differential evolution—— a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
          [6] 徐斌,陶莉莉,程武山.一種自適應多策略差分進化算法及其應用[J].化工學報,2016,67(12):5190-5198.
          [7] 徐斌,陳旭,陶莉莉,等.基于適應策略差分進化算法的化工反應動力學參數估值[J].化工進展,2018,37(6):2077-2083.
          [8] NAJARI S,GROF G,SAEIDI S,et al.Modeling and optimization of hydrogenation of CO2:estimation of kinetic parameters via artificial bee colony (ABC) and differential evolution (DE) algorithms[J].International Journal of Hydrogen Energy,2019,44(10):4630-4649.
          [9] 何鵬飛,李紹軍.融合差分進化算法的AEA算法及其在參數估計中的應用[J].化工學報,2014,65(12): 4857-4865.
          [10] BREST J,GREINER S,BOSKOVIC B,et al.Self- adapting control parameters in differential evolution:a comparative study on numerical benchmark problems [J].IEEE Transactions on Evolutionary Computation,2006,10(6):646-657.
          [11] TIAN M N,GAO X B,DAI C.Differential evolution with improved individual-based parameter setting and selection strategy[J].Applied Soft Computing,2017,56:286-297.
          [12] OMRANABC M G H,ENGELBRECHT A P,SALMAN A.Bare bones differential evolution[J].European Journal of Operational Research,2009,196(1):128-139.
          [13] KENNEDY J.Bare bones particle swarm[C]// Proceed- ings of the 2003 IEEE Swarm Intelligence Symposium (SIS’03).Indianapolis:IEEE,2003:80-87.
          [14] WANG H,RAHNAMAYAN S,SUN H,et al.Gaussian bare-bones differential evolution[J].IEEE Transactions on Cybernetics,2013,43(2):634-647.
          [15] 劉會宇,韓繼紅,袁霖,等.基于雙變異策略的自適應骨架差分進化算法[J].通信學報,2017,38(8):201- 212.
          [16] 彭虎,吳志健,周新宇,等.基于三角的骨架差分進化算法[J].計算機研究與發展,2015,52(12):2776-2788.
          [17] ZHONG J Q,SANDERSON A C.JADE:adaptive differential evolution with optional external archive[J]. IEEE Transactions on Evolutionary Computation,2009,13(5):945-958.
          [18] YAO X,LIU Y,LIU G M.Evolutionary programming made faster[J].IEEE Transactions on Evolutionary Computation,1999,3(2):82-102.
          [19] SUGANTHAN P N,HANSEN N,LIANG J J,et al. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[R]. Singapore:Nanyang Technological University,2005.
          [20] GARCIA S,FERNANDEZ A,LUENGO J,et al.Advanced nonparametric tests for multiple compare- sons in the design of experiments in computational intelligence and data mining:Experimental analysis of power[J].Information Sciences,2010,180(10):2044-2064.
          [21] WILCOXON F.Individual comparisons by ranking methods[M].Breakthroughs in statistics.New York:Springer,1992:196-202.
          [22] 商秀芹,盧建剛,孫優賢,等.一種基于偏好的多目標遺傳算法在動態模型參數辨識中的作用[J].化工學報,2008,59(7):1620-1624.
          [23] KOMORI Y,BUCKWAR E.Stochastic Runge-Kutta methods with deterministic high order for ordinary differential equations[J].BIT Numerical Mathematics,2013,53(3):617-639.
          [24] 江愛朋,邵之江,錢積新.基于簡約SQP和混合自動微分的反應參數優化[J].浙江大學學報(工學版),2004,38(12):1606-1614.
          [25] MARIA G.An adaptive strategy for solving kinetic model concomitant estimation-reduction problems [J].The Canadian Journal of Chemical Engineering,1989,67(5): 825-832.
          [26] 夏立榮,李潤學,劉啟玉,等.基于動態層次分析的自適應多目標粒子群優化算法以及應用[J].控制與決策,2015,30(2):215-222.
          文獻來源
          熊小峰, 劉嘯嬋, 郭肇祿, 張文生. 基于組合三角變異差分進化算法的化工參數辨識[J]. 華中科技大學學報(自然科學版), 2020, 48(9): 12-18
          DOI:10.13245/j.hust.200904
          澳门盘口