.png){kind=icon}
返回
Experiments with classification-based scalarizing functions in interactive multiobjective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2005.06.019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the classification information. In particular, we devote special interest to the differences the scalarizing functions have in the computational cost of guaranteeing Pareto optimality. It turns out that scalarizing functions with or without so-called augmentation terms have significant differences in this respect. We also collect a set of mostly nonlinear benchmark test problems that we use in the numerical comparisons. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
multiple objective programming
classification
interactive methods
test problems
guaranteeing pareto optimality
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
INTERACTIVE MULTIOBJECTIVE DECISION-MAKING BY THE SEQUENTIAL PROXY OPTIMIZATION TECHNIQUE - SPOT
OAAI
.png){kind=icon}
OAAI分享收藏Attitudes of Agricultural Experts Toward Genetically Modified Crops: A Case Study in Southwest Iran
PREAI
分享收藏
查看更多内容