基于ε约束理论和修复算子的多目标优化算法
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- 英文论文题名：ε-Constraint Theory and Repair Operator-Based Multiobjective Optimization Algorithm
- 论文作者：叶婵峰 ; 沈波 ; 林都 ; 董宏丽
- 英文论文作者：Chanfeng Ye ; Bo Shen ; Du Lin ; Hongli Dong ; School of Information Science and Technology ; Donghua University ; Institute of Complex Systems and Advanced Control ; Northeast Petroleum University ; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control
- 年：2017
- 作者机构：东华大学信息科学与技术学院;东北石油大学复杂系统与先进控制研究院;黑龙江省网络化与智能控制重点实验室;
- 论文关键词：约束多目标优化算法 ; ε约束理论 ; 修复算子
- 英文论文关键词：Constrained multiobjective optimization algorithms ; ε-constraint theory ; repair operator
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：中文
- 分类号：TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：693k
- 原文格式：D
- 会议级别：全国
摘要
多目标优化问题是当今科学和工程研究中的热点问题。针对目前约束多目标优化算法中存在的对不可行解的过度利用和参数难以整定等缺点,提出了一种基于ε约束理论和修复算子的多目标进化算法。在进化过程中,当不可行解约束违反值超过容忍阈值时,则从其邻域中选取更为优秀的个体将其修复,实现对不可行解的合理利用,从而提高种群质量。CTP系列测试实例验证结果表明所提算法在收敛性、多样性和稳定性方面均有较好的改进。
Nowadays, multiobjective optimization problem is a hot research issue both in science and engineering. Existing constrained multiobjective optimization algorithms have some drawbacks, such as overusing infeasible solutions and difficult in tuning parameters. In this paper, a multiobjective optimization algorithm is proposed by the aid of ε-constraint theory and repair operator. When the constraint violation value of the infeasible solution exceeds the tolerance threshold, a good individual is selected from its neighborhood to repair it so as to realize the reasonable utilization of infeasible solution and hence improve the population quality. Finally, it is shown by the results of CTP-series test instances that the proposed algorithm is improved in terms of convergence, diversity and stability.
Nowadays, multiobjective optimization problem is a hot research issue both in science and engineering. Existing constrained multiobjective optimization algorithms have some drawbacks, such as overusing infeasible solutions and difficult in tuning parameters. In this paper, a multiobjective optimization algorithm is proposed by the aid of ε-constraint theory and repair operator. When the constraint violation value of the infeasible solution exceeds the tolerance threshold, a good individual is selected from its neighborhood to repair it so as to realize the reasonable utilization of infeasible solution and hence improve the population quality. Finally, it is shown by the results of CTP-series test instances that the proposed algorithm is improved in terms of convergence, diversity and stability.
引文
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[11]Z.X.Yang,X.Y.Cai,Z.Fan,Epsilon Constrained Method for Constrained Multiobjective Optimization Problems:Some Preliminary Results.Genetic and Evolutionary Computation Conference,2014,14(10):1181-1186.
[12]S.Z.Martinez,C.A.C.Coello,A Multi-objective Evolutionary Algorithm based on Decomposition for Constrained Multi-objective Optimization.IEEE Congress on Evolutionary Computation,2014:429-436.
[13]T.Takahama,S.Sakai,N.Iwane,Constrained Optimization by the Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm.Advances in Artificial Optimization and Genetic Algorithm(2005’AI),Springer Berlin Heidelberg,LNAI 3809,2005:389-400.
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[15]R.Storn,K.Price,Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.Journal of Global Optimazation,1997,11(4):341-359.
[16]P.H.Tang,M.H.Tseng,Adaptive Directed Mutation for Real-coded Genetic Algorithms.Applied Soft Computing,2013,13(1):600-614.
[17]T.Ray,H.K.Singh,Isaacs A,et al,Infeasibility Driven Evolutionary Algorithm for Constrained Optimization.Springer Berlin Heidelberg,2009,198:155-175.
[2]K.Deb,A.Pratap,S.Agarwal,et al,A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II.IEEE Trans.on Evolutionary Computation,2002,6(2):182-197.
[3]Q.Zhang and H.Li,MOEA/D:A Multiobjective Evolutionary Algorithm Based on Decomposition.IEEETrans.on Evolutionary Computation,2007,11(6):712-731.
[4]H.L.Liu,C.D.Peng,F.Q.Gu,et al,A Constrained Multi-Objective Evolutionary Algorithm Based on Boundary Search and Archive.International Journal of Pattern Recognition,2016,30(1):1659002.
[5]S.B.Hamida,M.Schoenauer,ASCHEA:New Results Using Adaptive Segregational Constraint Handling.IEEEWorld Congress on Computational Intelligence(WCCI),2002,1:884-889.
[6]F.Z.Huang,L.Wang,Q.He,An effective co-evolutionary differential evolution for constrained optimization.Applied Mathematics and Computation,2007,186(1):340-356.
[7]F.Hoffmeister and J.Sprave,Problem-Independent Handling of Constraints by Use of Metric Penalty Functions.Proceedings of the 5th Annual Conference on Evolutionary Programming,1996:289-294.
[8]M.A.Jan,R.A.Khanum,N.M.Tairan,Performance of a Constrained Version of MOEA/D on CTP-series Test Instances.International Journal of Advanced Computer Science and Applications(IJACSA),2016,7(6):496-505.
[9]R.L.Becerra,C.A.C.Coello,Solving Hard Multiobjective-Constraint with Cultured Differential Evolution.Parallel Problem Solving from Nature-PPSN IX,2006,4093:543-552.
[10]T.Takahama,S.Sakai,Constrained Differential Evolution with an Archive and Gradient-Based Mutation.IEEE Congress on Evolutionary Computation,2010,3809(3):1-9.
[11]Z.X.Yang,X.Y.Cai,Z.Fan,Epsilon Constrained Method for Constrained Multiobjective Optimization Problems:Some Preliminary Results.Genetic and Evolutionary Computation Conference,2014,14(10):1181-1186.
[12]S.Z.Martinez,C.A.C.Coello,A Multi-objective Evolutionary Algorithm based on Decomposition for Constrained Multi-objective Optimization.IEEE Congress on Evolutionary Computation,2014:429-436.
[13]T.Takahama,S.Sakai,N.Iwane,Constrained Optimization by the Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm.Advances in Artificial Optimization and Genetic Algorithm(2005’AI),Springer Berlin Heidelberg,LNAI 3809,2005:389-400.
[14]K.Miettinen,Nonlinear Multiobjective Optimization.Boston,Massachuisetts:Kluwer Academic Publishers,1999.
[15]R.Storn,K.Price,Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.Journal of Global Optimazation,1997,11(4):341-359.
[16]P.H.Tang,M.H.Tseng,Adaptive Directed Mutation for Real-coded Genetic Algorithms.Applied Soft Computing,2013,13(1):600-614.
[17]T.Ray,H.K.Singh,Isaacs A,et al,Infeasibility Driven Evolutionary Algorithm for Constrained Optimization.Springer Berlin Heidelberg,2009,198:155-175.