基于权值向量的偏好多目标优化方法

doi:10.3969/j.issn.0372-2112.2016.11.011

电子学报 ›› 2016, Vol. 44 ›› Issue (11): 2639-2645.DOI: 10.3969/j.issn.0372-2112.2016.11.011

基于权值向量的偏好多目标优化方法

张兴义^1,2, 蒋小三², 张磊^1,2

1. 安徽大学计算机与科学技术学院, 安徽合肥 230039;
2. 安徽大学计算智能与信号处理教育部重点实验室, 安徽合肥 230039

收稿日期:2015-05-11 修回日期:2015-08-24 出版日期:2016-11-25
- 通讯作者:
- 张磊
- 作者简介:
- 张兴义,男,1982年生,2009年毕业于华中科技大学自动化学院,获得博士学位,现为安徽大学计算机科学与技术学院副教授、博士生导师.主要研究方向包括非传统计算模型与算法、多目标优化算法及应用、膜计算.E-mail:xyzhanghust@gmail.com;蒋小三,男,1988年出生,安徽安庆人,现为安徽大学计算机应用技术专业硕士研究生.研究方向为偏好多目标进化算法.E-mail:westsxj88@126.com
- 基金资助:
- 国家自然科学基金 (No.61272152，No.61502001）; 安徽大学学校学术与技术带头人引进工程 (No.J10117700050）

A Weight Vector Based Multi-objective Optimization Algorithm with Preference

ZHANG Xing-yi^1,2, JIANG Xiao-san², ZHANG Lei^1,2

1. School of Computer Science and Technology, Anhui University, Hefei, Anhui 230039, China;
2. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, Anhui 230039, China

Received:2015-05-11 Revised:2015-08-24 Online:2016-11-25 Published:2016-11-25

摘要/Abstract

摘要：

偏好多目标优化方法是多目标优化领域的一个重要分支，其主要目的是仅搜索Pareto前沿面上部分区域内决策者感兴趣的解.基于MOEA/D算法根据预先设定的均匀分布的权值向量搜索Pareto最优前沿面的思想，本文提出了一种基于权值向量的偏好多目标优化方法，该方法通过引入具有偏好信息的权值向量，使算法仅搜索偏好点附近的解.仿真实验结果表明，与现有偏好多目标优化算法相比，本文方法具有支持多偏好点、偏好区域大小可控、偏好点位置无特别要求及偏好解具有更好收敛性的优势.

关键词: 多目标优化, 偏好多目标优化算法, 权值向量, 偏好解

Abstract:

Multi-objective optimization algorithms with preference are an important branch of multi-objective optimization.Its main aim is to find the Pareto optimal solutions in local regions interested by Decision Makers.Based on the idea of MOEA/D algorithm to search the Pareto front according to uniformly distributed weight vector,this paper proposes a weight vector based multi-objective optimization algorithm with preference.In the proposed method,the weight vector with preference is designed,by which the solutions around the preferred point interested by Decision Maker are found.Compared with existing algorithms,the simulation results verify that the proposed method can support multiple reference points,flexibly control the extent of preferred region,have no special requirement of the position of preference points and achieve better converge.

Key words: multi-objective optimization, multi-objective optimization algorithms with preference, weight vector, preferred solution

中图分类号:

TP18

张兴义, 蒋小三, 张磊. 基于权值向量的偏好多目标优化方法[J]. 电子学报, 2016, 44(11): 2639-2645.

ZHANG Xing-yi, JIANG Xiao-san, ZHANG Lei. A Weight Vector Based Multi-objective Optimization Algorithm with Preference[J]. Acta Electronica Sinica, 2016, 44(11): 2639-2645.

参考文献

[1] 崔逊学.多目标进化算法及其应用[M].北京:国防工业出版社,2006.17-25.
[2] Zhou A M,Qu B Y,Li H,et al.Multiobjective evolutionary algorithms:a survey of the state of the art[J].Swarm and Evolutionary Computation,2011,1(1):32-49.
[3] Rachmawati L,Srinivasan D.Preference incorporation in multiobjective evolutionary algorithms:a survey[A].Proceedings of the Congress on Evolutionary Computation[C].Canada:IEEE,2007.962-968.
[4] Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[5] Zitzler E,Laumanns M,Thiele L.SPEA2:Improving the Strength Pareto Evolutionary Algorithm[M].Berlin:Springer,2002.95-100.
[6] Zhang Q F,Li H.MOEA/D:A multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,11(6):712-731.
[7] Zhang X Y,Tian Y,Jin Y C.A knee point driven evolutionary algorithm for many-objective optimization[J].IEEE Transactions on Evolutionary Computation,2014,dol.10.1109/TEVC.2014.2378512.
[8] Wierzbicki A P.The Use of Reference Objectives in Multiobjective Optimization[M].Berlin:Springer,1980.468-486.
[9] Branke J,Deb K.Integrating User Preferences into Evolutionary Multi-Objective Optimization[M].Berlin:Springer,2005.461-477.
[10] Deb K,Sundar J,Udaya Bhaskara R N,et al.Reference point based multi-objective optimization using evolutionary algorithms[J].International Journal of Computational Intelligence Research,2006,2(3):273-286.
[11] 巩敦卫,王更星,孙晓燕.高维多目标优化问题融入决策者偏好的集合进化优化方法[J].电子学报,201342(5):933-939. Gong Dunwei,Wang Gengxin,Sun Xiaoyan.Set-based evolution optimization algorithms integrating decision-maker's preferences for many-objective optimization problems[J].Acta Electronica Sinica,2013,42(5):933-939.(in Chinese)
[12] Deb K,Kumar A.Light beam search based multi-objective optimization using evolutionary algorithms[A].Proceedings of the Congress on Evolutionary Computation[C].Singapore:IEEE,2007.2125-2132.
[13] Molina J,Santana L V,Hernández-Díaz A G,et al.g-dominance:reference point based dominance for multiobjective metaheuristics[J].European Journal of Operational Research,2009,197(2):685-692.
[14] Ben Said L,Bechikh S,Ghédira K.The r-dominance:a new dominance relation for interactive evolutionary multicriteria decision making[J].IEEE Transactions on Evolutionary Computation,2010,14(5):801-818.
[15] 邱飞岳,吴裕市,邱启仓,王丽萍.基于双极偏好占优的高维目标进化算法[J].软件学报,2013,24(3):476-489. Qiao Feiyue,Wu Yushi,Qiu Qicang,Wang Liping.Many-objective evolution algorithm based on bipolar preferences dominance[J].Journal of Software,2013,24(3):476-489.(in Chinese)
[16] 郑金华,谢谆志.关于如何用角度信息引入决策者偏好的研究[J].电子学报,2014,42(11):2239-2246. Zheng Jinhua,Xie Zhunzhi.A study on how to use angle information to induce decision maker's preference[J].Acta Electronica Sinica,2014,42(11):2239-2246.(in Chinese)
[17] Huband S,Hingston P,Barone L,et al.Review of multi-objective test problems and a scalable test problem toolkit[J].IEEE Transactions on Evolutionary Computation,2006,10(55):477-506.
[18] Zhang X Y,Tian Y,Cheng R,et al.An efficient approach to non-dominated sorting for evolutionary multi-objective optimization[J].IEEE Transactions on Evolutionary Computation,2015,19(2):201-213.
[19] Deb K,Jain S.Running performance metrics for evolutionary multi-objective optimization[A].Proceedings of the Fourth Asia-Pacific Conference on simulated Evolution and Learning[C].Singapore:IEEE,2003,13-20.

基于权值向量的偏好多目标优化方法

A Weight Vector Based Multi-objective Optimization Algorithm with Preference

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