Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems*

Chinese Journal of Chemical Engineering ›› 2012, Vol. 20 ›› Issue (6): 1074-1080.

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*

摆亮^1,2, 王钧炎³, 江永亨^1,2, 黄德先^1,2

1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
3. Marvell Technology (Shanghai) Ltd, Shanghai 201203, China

收稿日期:2012-05-28 修回日期:2012-07-20 出版日期:2012-12-28 发布日期:2012-12-28
通讯作者: HUANG Dexian,E-mail:huangdx@tsinghua.edu.cn
基金资助:
Supported by the National Basic Research Program of China (2012CB720500);the National Natural Science Foundation of China (60974008)

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*

BAI Liang^1,2, WANG Junyan³, JIANG Yongheng^1,2, HUANG Dexian^1,2

1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
3. Marvell Technology (Shanghai) Ltd, Shanghai 201203, China

Received:2012-05-28 Revised:2012-07-20 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported by the National Basic Research Program of China (2012CB720500);the National Natural Science Foundation of China (60974008)

摘要/Abstract

摘要： In this paper,an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields.In order to improve the global searching ability and convergence speed,IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm.Moreover,the feasibility rules are used to handle constraints,which do not require additional parameters and can guide the population to the feasible region quickly.The effectiveness of hybridization mechanism of IHDE-EDA is first discussed,and then simulation and comparison based on three benchmark problems demonstrate the efficiency,accuracy and robustness of IHDE-EDA.Finally,optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

关键词: differential evolution, estimation of distribution, hybrid evolution, mixed-coding, feasibility rules

Abstract: In this paper,an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields.In order to improve the global searching ability and convergence speed,IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm.Moreover,the feasibility rules are used to handle constraints,which do not require additional parameters and can guide the population to the feasible region quickly.The effectiveness of hybridization mechanism of IHDE-EDA is first discussed,and then simulation and comparison based on three benchmark problems demonstrate the efficiency,accuracy and robustness of IHDE-EDA.Finally,optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

Key words: differential evolution, estimation of distribution, hybrid evolution, mixed-coding, feasibility rules

摆亮, 王钧炎, 江永亨, 黄德先. Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1074-1080.

BAI Liang, WANG Junyan, JIANG Yongheng, HUANG Dexian. Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1074-1080.

参考文献

1 Grossmann,i.e.,"Review of nonlinear mixed-integer and disjunctive programming techniques",Optim.Eng.,3 (3),227-252 (2002).
2 B ck,T.,Evolutionary Algorithms in Theory and Practice,Oxford University Press,New York (1996).
3 Nolle,L.,K ppen,M.,Schaefer,G.,Abraham,A.,Intelligent computational optimization in engineering: Techniques and Applications,Springer,Germany (2011).
4 Storn,R.,Price,K.,"Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces",J.Global Optim.,11 (4),341-359 (1997).
5 Larranaga,P.,Lozano,J.A.,Estimation of distribution algorithms: A new tool for evolutionary computation,Kluwer Academic Publishers,Boston (2002).
6 Chang,W.,Yeh,W.,Huang P.,"A hybrid immune-estimation distribution of algorithm for mining thyroid gland data",Expert Syst.Appl.,37 (3),2066-2071 (2010).
7 Pena,J.M.,Robles,V.,Larranaga,P.,Herves,V.,Rosales,F.,Perez,M.S.,"GA-EDA: Hybrid evolutionary algorithm using genetic and estimation of distribution algorithms",Lect.Notes Comput.Sci.,3029,361-371 (2004).
8 Sun,J.,Zhang,Q.,Tsang,E.P.K.,"DE/EDA: A new evolutionary algorithm for global optimization",Inform.Sciences,169 (3-4),249-262 (2005).
9 He,D.K.,Wang,F.L.,Mao,Z.Z.,"A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect",Int.J.Elec.Power,30 (1),31-38 (2008).
10 Liao,T.W.,"Two hybrid differential evolution algorithms for engineering design optimization",Appl.Soft Comput.,10 (4),1188-1199 (2010).
11 Price,K.V.,"Differential evolution vs.the functions of the 2nd ICEO",In: Proc.1997 IEEE Inter.Conf.on Evolut.Comput.(ICEC 97),USA,153-157 (1997).
12 Deb,K.,"An efficient constraint handling method for genetic algorithms",Comput.Method Appl.M.,186 (2-4),311-338 (2000)
13 Lin,Y.,Hwang,K.,Wang,F.,"A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems",Comput.Math.Appl.,47 (8-9),1295-1307 (2004).
14 Angira,R.,Babu,B.V.,"Optimization of process synthesis and design problems: A modified differential evolution approach",Chem.Eng.Sci.,61 (14),4707-4721 (2006).
15 Bean,J.C.,"Genetic algorithms and random keys for sequencing and optimization",Informs J.Comput.,6 (2),154-160 (1994).
16 Rao,S.S.,Engineering Optimization,Wiley,New York (1996).
17 Coello,C.,"Use of a self-adaptive penalty approach for engineering optimization problems",Comput.Ind.,41 (2),113-127 (2000).
18 Coello,C.,Montes,E.M.,"Constraint-handling in genetic algorithms through the use of dominance-based tournament selection",Adv.Eng.Inform.,16 (3),193-203 (2002).
19 He,Q.,Wang,L.,"An effective co-evolutionary particle swarm optimization for constrained engineering design problems",Eng.Appl.Artif.Intel.,20 (1),89-99 (2007).
20 Huang,F.Z.,Wang,L.,He,Q.,"An effective co-evolutionary differential evolution for constrained optimization",Appl.Math.Comput.,186 (1),340-356 (2007).
21 Wang,L.,Li,L.P.,"An effective differential evolution with level comparison for constrained engineering design",Struct.Multidiscip.O.,41 (6),947-963 (2010).
22 Belegundu,A.D.,"A study of mathematical programming methods for structural optimization",Ph.D.Thesis,University of Iowa,USA (1982).
23 Grossmann,I.E.,Sargent,R.,"Optimum design of multipurpose chemical-plants",Ind.Eng.Chem.Process Des.Dev.,18 (2),343-348 (1979).
24 Costa,L.,Oliveira,P.,"Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems",Comput.Chem.Eng.,25 (2-3),257-266 (2001).
25 Lin,Y.,Hwang,K.,Wang,F.,"A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems",Comput.Math.Appl.,47 (8-9),1295-1307 (2004).
26 Luo,Y.Q.,Yuan,X.G.,Liu,Y.J.,"An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints",Comput.Chem.Eng.,31 (3),153-162 (2007).
27 Bai,L.,Jiang,Y.H.,Huang,D.X.,"A novel two-level optimization framework based on constrained ordinal optimization and evolutionary algorithms for scheduling of multipipeline crude oil blending",Ind.Eng.Chem.Res.,51 (26),9078-9093 (2012).
28 Bai,L.A.,Jiang,Y.H.,Huang,D.X.,"A novel scheduling strategy for crude oil blending",Chin.J.Chem.Eng.,18 (5),777-786 (2010).(a) TBP curves of crude oils before blending (b) TBP curves of mixed feed oils after blending Figure 4 The improvement of TBP (temperature of boiling point) curves of crude oils under the optimized schedule

[1]	Lili Tao, Bin Xu, Zhihua Hu, Weimin Zhong. Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm[J]. , 2017, 25(8): 983-991.
[2]	Xu Chen, Wenli Du, Feng Qian. Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms[J]. Chinese Journal of Chemical Engineering, 2016, 24(11): 1600-1608.
[3]	年笑宇, 王振雷, 钱锋. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. Chinese Journal of Chemical Engineering, 2013, 21(5): 537-543.
[4]	赵超, 许巧玲, 林思铭, 李学来. Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems[J]. Chinese Journal of Chemical Engineering, 2013, 21(2): 155-162.
[5]	孙帆, 钟伟民, 程辉, 钱锋 . Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes[J]. Chinese Journal of Chemical Engineering, 2013, 21(1): 64-71.
[6]	吕文祥, 朱鹰, 黄德先, 江永亨, 金以慧. A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process[J]. , 2010, 18(1): 66-79.
[7]	胡春平, 颜学峰. An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation[J]. , 2009, 17(2): 232-240.
[8]	吴燕玲, 卢建刚, 孙优贤. An Improved Differential Evolution for Optimization of Chemical Process[J]. , 2008, 16(2): 228-234.

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价