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浙江大学学报(理学版)  2018, Vol. 45 Issue (1): 23-28    DOI: 10.3785/j.issn.1008-9497.2018.01.005
数学与计算机科学     
生物地理学优化算法中基于Zoutendijk可行方向法的变异算子设计
王月娇, 刘三阳
西安电子科技大学 数学与统计学院, 陕西 西安 710126
A new mutation operator designed by Zoutendijk feasible direction method in biogeography-based optimization
WANG Yuejiao, LIU Sanyang
School of Mathematics and Statistics, Xidian University, Xi'an 710126, China
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摘要: 根据约束优化问题的全局收敛性要求,基于传统优化与智能优化,设计了一种基于Zoutendijk可行方向法的新型变异算子,并将其应用于生物地理学优化算法,构建了一种用混合优化算法求解优化问题的方法.通过算子设计策略的理论验证、智能算法的收敛性分析及6个不同类型算例的仿真试验,证明此自适应求解优化问题机制具有实效性.
关键词: 约束优化Zoutendijk可行方向法变异算子生物地理学优化算法    
Abstract: The article deals with a class of nonlinear constraint programming problems, and a new hybrid biogeography-based optimization algorithm is proposed to search for the value of the variables. To meet the global convergence requirements of the constraint optimization problem, a new efficient mutation operator based on Zoutendijk feasible direction method is designed by integrating the advantages of traditional and intelligent optimization so that it can generate high quality potential offspring. Then, a hybrid biogeography-based optimization algorithm is constructed to search for the optimal solution of the constraint programming. Furthermore, a specific example is demonstrated which shows that our new mechanism can be easily used to force the individuals moving toward the feasible region and improve the feasible solutions gradually. The theoretical identification of operator design strategy, convergence analysis of intelligent algorithm and simulation experiment on six different types of numerical examples verify the effectiveness of the proposed adaptive mechanism in solving optimization problem.
Key words: constraint optimization    Zoutendijk feasible direction method    mutation operator    biogeography-based optimization
收稿日期: 2016-06-28 出版日期: 2017-12-15
CLC:  O224  
基金资助: 国家自然科学基金资助项目(61373174);中央高校基本科研业务费专项资金资助项目(JB150716).
作者简介: 王月娇(1991-),ORCID:http://orcid.org/0000-0001-5910-9439,女,硕士,主要从事最优化理论与智能算法设计研究,E-mail:xd07101051@126.com.
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王月娇, 刘三阳. 生物地理学优化算法中基于Zoutendijk可行方向法的变异算子设计[J]. 浙江大学学报(理学版), 2018, 45(1): 23-28.

WANG Yuejiao, LIU Sanyang. A new mutation operator designed by Zoutendijk feasible direction method in biogeography-based optimization. Journal of ZheJIang University(Science Edition), 2018, 45(1): 23-28.

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https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2018.01.005        https://www.zjujournals.com/sci/CN/Y2018/V45/I1/23

[1] SIMON D.Biogeography-based optimization[J].IEEE Transactions on Evolutionary Computation,2008,12(6):702-713.
[2] 王存睿,王楠楠,段晓东,等.生物地理学优化算法综述[J].计算机科学,2010,37(7):34-38. WANG C R,WANG N N,DUAN X D,et al.Survey of biogeography-based optimization[J].Computer Science,2010,37(7):34-38.
[3] 封全喜.生物地理学优化算法研究及其应用[D].西安:西安电子科技大学,2014. FENG Q X.Research on Biogeography-Based Optimization and Its Application[D].Xi'an:Xidian University,2014.
[4] ZHOU X,LIU Y H,LI B,et al.Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks[J].Physica A Statistical Mechanics and Its Applications,2015,436:430-442.
[5] GOLAFSHANI E M.Introduction of biogeography-based programming as a new algorithm for solving problems[J].Applied Mathematics and Computation,2015,270(C):1-12.
[6] 张建科.生物地理学优化算法研究[J].计算机工程与设计,2011,32(7):2497-2500. ZHANG J K.Research on optimization algorithm for biogeography[J].Computer Engineering and Design,2011,32(7):2497-2500.
[7] YOU X M,HAO F C,MA Y H.A hybrid differential evolution algorithm solving complex multimodal optimization problems[J].Journal of Information and Computational Science,2015,12(13):5175-5182.
[8] ZHANG S,LIU S Y.Artificial bee colony algorithm with improved search equations[J].Journal of Information and Computational Science,2015,12(10):4069-4076.
[9] WAN Z P,WANG G M,SUN B.A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems[J].Compstat 2006-Proceedings in Computational Statistics,2013(8):26-32.
[10] LI H C.An evolutionary algorithm for multi-criteria inverse optimal value problems using a bilevel optimization model[J].Applied Soft Computing,2014,23(23):308-318.
[11] KUO R J,LEE Y H,ZULVIA F E,et al. Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm[J].Applied Mathematics and Computation,2015,266(C):1013-1026.
[12] 唐焕文,秦学志.实用最优化方法[M].第3版.大连:大连理工大学出版社,2004.TANG H W,QIN X Z.Practical Methods of Optimization[M].3rd ed. Dalian:Dalian University of Technology Press,2004.
[13] 李和成.非线性双层规划问题的遗传算法研究[D].西安:西安电子科技大学,2009. LI H C.Research on Genetic Algorithm of Nonlinear Bilevel Programming[D].Xi'an:Xidian University,2009.
[14] WANG Y P,DANG C Y.An evolutionary algorithm for global optimization based on level-set evolution and Latin squares[J].IEEE Transactions on Evolutionary Computation,2007(11):579-595.
[15] WANG Y P,LI H,DANG C Y.A new evolutionary algorithm for a class of nonlinear bilevel programming problems and its global convergence[J].Informs Journal on Computing,2011,23(4):618-629.
[16] WANG Y P,JIAO Y C,LI H.An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme[J].IEEE Transactions on Systems,Man and Cybernetics,2005,35(2):221-232.
[17] ZHONG W J,XU N R.Boltzmann machine approach of bilevel programming[J].Journal of Systems Engineering,1995,10(1):7-13.
[18] ZHENG P E,LIUG H,LI R B.The solution of a class of bilevel programming based on hierarchical optimization algorithm[J].Systems Engineering and Electronics,2005,27(4):662-665.
[19] BJORNDAL M,JORNSTEN K.The deregulated electricity market viewed as a bilevel programming problem[J].Journal of Global Optimization,2005,33(3):465-475.
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