基于集合的高维多目标优化问题的进化算法

doi:10.3969/j.issn.0372-2112.2014.01.012

电子学报

基于集合的高维多目标优化问题的进化算法

巩敦卫, 季新芳, 孙晓燕

中国矿业大学信息与电气工程学院, 江苏徐州 221008

收稿日期:2012-11-26 修回日期:2013-03-16 出版日期:2014-01-25
- 作者简介:
- 巩敦卫 男，1970年3月出生，江苏铜山人.博士，教授，博士生导师，1992年、1995年和1999年分别在中国矿业大学、北京航空航天大学和中国矿业大学获理学学士、工学硕士和工学博士学位.主要从事基于搜索的软件工程、智能优化与控制等方面的研究. E-mail:dwgong@vip.163.com 季新芳 女，1987年8月出生，江苏泰州人.2010获中国矿业大学工学学士学位.现为中国矿业大学控制理论与控制工程硕士研究生，主要从事多目标优化方面的研究. E-mail:mimosa_-615615@126.com
- 基金资助:
- 中央高校基本科研业务费专项资金资助 (No.2013XK09）; 国家自然科学基金 (No.61105063）

Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms

GONG Dun-wei, JI Xin-fang, SUN Xiao-yan

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008 , China

Received:2012-11-26 Revised:2013-03-16 Online:2014-01-25 Published:2014-01-25
- Supported by:
- Fundamental Research Funds for the Central Universities (No.2013XK09); National Natural Science Foundation of China (No.61105063)

摘要/Abstract

摘要： 由于高维多目标优化问题包含的目标很多，已有的方法往往难以解决该问题.本文提出一种有效解决该问题的基于集合的进化算法，该方法以超体积、分布度，以及延展度为新的目标，将原优化问题转化为3目标优化问题；定义基于集合的Pareto占优关系，设计体现用户偏好的适应度函数；此外，还提出集合进化策略.将所提方法应用于4个基准高维多目标优化问题，并与其他2种方法比较，实验结果表明了所提方法的优越性.

关键词: 集合进化, 用户偏好, 进化算法, 高维多目标优化, 集合进化, 用户偏好

Abstract: Previous methods are difficult to tackle a many-objective optimization problem since it contains many objectives.A set-based evolutionary algorithm was proposed to effectively solve the above problem in this study.In the proposed method,the original optimization problem was first transformed into a tri-objective one by taking such indicators as hyper-volume,distribution and spread as three new objectives;thereafter,a set-based Pareto dominance relation was defined,and a fitness function reflecting a user's preference was designed;additionally,set-based evolutionary strategies were suggested.The proposed method was applied to four benchmark many-objective optimization problems and compared with the other two methods.The experimental results show its advantages.

Key words: set-based evolution, user preference, evolutionary algorithm, many-objective optimization, set-based evolution, user preference

中图分类号:

TP301

巩敦卫, 季新芳, 孙晓燕. 基于集合的高维多目标优化问题的进化算法[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2014.01.012.

GONG Dun-wei, JI Xin-fang, SUN Xiao-yan . Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2014.01.012.

参考文献

[1] 南国芳,陈忠楠.基于进化优化的移动感知节点部署算法[J].电子学报,2012,40(5):1017-1022. Nan Guo-fang,Chen Zhong-nan.Deployment algorithm of mobile sensing nodes based on evolutionary optimization[J].Acta Electronica Sinica,2012,40(5):1017-1022.(in Chinese)

[2] S F Adra,A Hamody,I Griffin,P J Fleming.A hybrid multi-objective evolutionary algorithm using an inverse neural network for aircraft control system design [A].Proceedings of IEEE Congress on Evolutionary Computation [C].Edinburgh:IEEE,2005.1-8.

[3] Singh H K,Isaacs A,Ray T.A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems[J].IEEE Transactions on Evolutionary Computation,2011,15(4):539-556.

[4] Zou X F,Chen Y,Liu M Z,et al.A new evolutionary algorithm for solving many-objective optimization problems[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2008,38(5):1402-1412.

[5] T Murata,A Taki.Examination of the performance of objective reduction using correlation-based weighted-sum for many objective knapsack problems [A].Proceedings of 10th International Conference on Hybrid Intelligent Systems [C].Atlanta:IEEE,2010.175-180.

[6] J H Zheng,K Li,H Lv.Objective reduction based on the least square method for large-dimensional multi-objective optimization problem [A].Proceedings of 5th International Conference on Natural Computation [C].Tianjin:IEEE,2009.350-354.

[7] Zitzler E,Thiele L,Bader J.On set-based multiobjective optimization [J].IEEE Transactions on Evolutionary Computation,2010,14(1):58-79.

[8] J Bader,D Brockhoff,S Welten,E Zitzler.On using population of sets in multiobjective optimization [A].Proceedings of 5th International Conference on Evolutionary Multi-objective Optimization [C].Nantes:Springer,2009.5467:140-154.

[9] Zitzler E,Deb,K,Thiele,L.Comparison of multiobjective evolutionary algorithms:empirical results[J].Evolutionary Computation,2008,8(2):173-195.

[10] 雷德明,严新平,吴智铭.多目标混沌进化算法[J].电子学报,2006,34(6):1142-1145. Lei De-ming,Yan Xin-ping,Wu Zhi-ming.Multi-objective chaotic evolutionary algorithm[J].Acta Electronica Sinica,2006,34(6):1142-1145.(in Chinese)

[11] 李密青,郑金华,谢炯亮,杨平,李晶.一种MOEA分布度的逐步评价方法[J].电子学报,2008,36(10):1986-1991. Li Mi-qing,Zheng Jin-hua,Xie Jiong-liang,Yang Ping,LI Jing.A stepwise diversity metric for MOEA[J].Acta Electronica Sinica,2008,36(10):1986-1991.(in Chinese)

[12] Zitzler E.Evolutionary Algorithms Multiobjective Optimization:Methods and Applications [D].Zurich:Swiss Federal Institute of Technology,1999.

[13] Schoot J R.Fault Tolerant Design Using Single and Multicriteria Genetic Algorithms Optimization [D].Cambridge:Massachusetts Institute of Technology,1995.

[14] Bentley P J,Wakefield J P.Finding acceptable solutions in the Pareto-optimal range using multiobjective genetic algorithms [A].Soft Computing in Engineering Design and Manufacturing [C].Springer,1998,Part 5:231-240.

[15] M G Fabre,G T Pulido,C A C Coello.Ranking methods for many-objective optimization [A].Proceedings of 8th Mexican International Conference on Artificial Intelligence [C].Guanajuato:Springer,2009.5845:633-645.

[16] Bader J,Zitzler E.HypE:An algorithm for fast hypervolume-based many-objective optimization[J].Evolutionary Computation,2011,19(1):45-76.

[17] Bader J,Zitzler E.A hypervolume-based optimizer for high-dimensional objective spaces [A].New Developments in Multiple Objective and Goal Programming [C].Springer,2010,638:35-54.

[18] Deb K,Thiele L,Laumanns M,Zitzler E.Scalable Test Problems for Evolutionary Multiobjective Optimization [R].Institut fur Technische Informatik und Kommunikationsentze (TIK) ETH Zurich,Technical Report:TIK-Report,No.112,2001.

基于集合的高维多目标优化问题的进化算法

Solving Many-Objective Optimization Problems Using Set-Based Evolutionary Algorithms

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