Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation

被引：0
|
作者：
Rudolph, Guenter [1 ]
Trautmann, Heike [2 ]
Sengupta, Soumyadip [3 ]
Schuetze, Oliver [4 ]
机构：
[1] Tech Univ Dortmund, Fak Informat, D-44221 Dortmund, Germany
[2] Tech Univ Dortmund, Fak Stat, D-44221 Dortmund, Germany
[3] Jadavpur Univ, Kolkata, India
[4] CINVESTAV, IPN, Dept Computac, Mexico City 14000, DF, Mexico
来源：
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, EMO 2013 | 2013年 / 7811卷
关键词：
multiobjective optimization; evolutionary multiobjective algorithm; evenly spaced Pareto front approximation; averaged Hausdorff measure; triangulation; GENETIC ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In some technical applications like multiobjective online control an evenly spaced approximation of the Pareto front is desired. Since standard evolutionary multiobjective optimization (EMO) algorithms have not been designed for that kind of approximation we propose an archive-based plug-in method that builds an evenly spaced approximation using averaged Hausdorff measure between archive and reference front. In case of three objectives this reference font is constructed from a triangulated approximation of the Pareto front from a previous experiment. The plug-in can be deployed in online or offline mode for any kind of EMO algorithm.
引用
收藏
页码：443 / 458
页数：16
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