Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems

被引：0
|
作者：
Trautmann, Heike [1 ]
Rudolph, Guenter [1 ]
Dominguez-Medina, Christian [2 ]
Schuetze, Oliver [3 ]
机构：
[1] Tech Univ Dortmund, Fak Stat, D-44221 Dortmund, Germany
[2] CINVESTAV IPN, Dept Ingn Elect, Secc Computac, Mexico City 07738, DF, Mexico
[3] CINVESTAV IPN, Dept Comp Sci, Mexico City 07360, DF, Mexico
来源：
EVOLVE - A BRIDGE BETWEEN PROBABILITY, SET ORIENTED NUMERICS, AND EVOLUTIONARY COMPUTATION II | 2013年 / 175卷
关键词：
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：
The averaged Hausdorff distance Delta(p) is a performance indicator in multi-objective evolutionary optimization which simultaneously takes into account proximity to the true Pareto front and uniform spread of solutions. Recently, the multi-objective evolutionary algorithm Delta(p)-EMOA was introduced which successfully generates evenly spaced Pareto front approximations for bi-objective problems by integrating an external archiving strategy into the SMS-EMOA based on Delta(p). In this work a conceptual generalization of the Delta(p)-EMOA for higher objective space dimensions is presented and experimentally compared to state-of-the art EMOA as well as specialized EMOA variants on three-dimensional optimization problems.
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页码：89 / +
页数：4
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