A new perspective on multiobjective optimization by enhanced normalized normal constraint method

被引:60
作者
Sanchis, J. [1 ]
Martinez, M. [1 ]
Blasco, X. [1 ]
Salcedo, J. V. [1 ]
机构
[1] Polytechn Univ Valencia, Dept Syst Engn & Control, Valencia, Spain
关键词
multiobjective optimization; pareto frontier; nonlinear optimization; engineering design;
D O I
10.1007/s00158-007-0185-4
中图分类号
TP39 [计算机的应用];
学科分类号
081203 [计算机应用技术]; 0835 [软件工程];
摘要
In industrial applications, several objectives are often managed simultaneously (e.g., minimizing the cost and the weight of a mechanical structure satisfying some constraints). Although lots of optimization studies deal with only one objective, this approach is often not realistic for engineering optimization. Therefore, improvements in multiobjective optimization methods are required. This paper presents the formulation of a new utopia hyperplane that improves the proposal of the original normalized normal constraint method using two approaches: a redefinition of the anchor points and an exact linear transformation between the design objectives space and the normalized space. Both approaches always produce a normalized space with equal scales that improves the even distribution of the solutions over the Pareto frontier. Examples of the method proposed are presented related with mechanical engineering and structure design including a challenging non-convex Pareto frontier.
引用
收藏
页码:537 / 546
页数:10
相关论文
共 14 条
[1]
Quality utility - A compromise programming approach to robust design [J].
Chen, W ;
Wiecek, MM ;
Zhang, J .
JOURNAL OF MECHANICAL DESIGN, 1999, 121 (02) :179-187
[2]
Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems [J].
Das, I ;
Dennis, JE .
SIAM JOURNAL ON OPTIMIZATION, 1998, 8 (03) :631-657
[3]
FONSECA CM, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P416
[4]
Implicit Niching in a Learning Classifier System: Nature's Way [J].
Horn, Jeffrey ;
Goldberg, David E. ;
Deb, Kalyanmoy .
EVOLUTIONARY COMPUTATION, 1994, 2 (01) :37-66
[5]
An interactive fuzzy multi-objective optimization method for engineering design [J].
Huang, Hong-Zhong ;
Gu, Ying-Kui ;
Du, Xiaoping .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2006, 19 (05) :451-460
[6]
DEFECTIVENESS OF WEIGHTING METHOD IN MULTICRITERION OPTIMIZATION OF STRUCTURES [J].
KOSKI, J .
COMMUNICATIONS IN APPLIED NUMERICAL METHODS, 1985, 1 (06) :333-337
[7]
Immune network simulation with multiobjective genetic algorithms for multidisciplinary design optimization [J].
Kurapati, A ;
Azarm, S .
ENGINEERING OPTIMIZATION, 2000, 33 (02) :245-260
[8]
Survey of multi-objective optimization methods for engineering [J].
Marler, RT ;
Arora, JS .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2004, 26 (06) :369-395
[9]
Global and well-distributed Pareto frontier by modified normalized normal constraint methods for bicriterion problems [J].
Martinez, M. ;
Sanchis, J. ;
Blasco, X. .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2007, 34 (03) :197-209
[10]
Smart Pareto filter: Obtaining a minimal representation of multiobjective design space [J].
Mattson, CA ;
Mullur, AA ;
Messac, A .
ENGINEERING OPTIMIZATION, 2004, 36 (06) :721-740