.png){kind=icon}
返回
Evolutionary multiobjective optimization using an outranking-based dominance generalization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cor.2009.06.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One aspect that is often disregarded in the current research on evolutionary multiobjective optimization is the fact that the solution of a multiobjective optimization problem involves not only the search itself, but also a decision making process. Most current approaches concentrate on adapting an evolutionary algorithm to generate the Pareto frontier. In this work, we present a new idea to incorporate preferences into a multi-objective evolutionary algorithm (MOEA). We introduce a binary fuzzy preference relation that expresses the degree of truth of the predicate x is at least as good as y. On this basis, astrict preference relation with a reasonably high degree of credibility can be established on any population. An alternative x is not strictly outranked if and only if there does not exist an alternative y which is strictly preferred to x. It is easy to prove that the best solution is not strictly outranked. For validating our proposed approach, we used the non-dominated sorting genetic algorithm II (NSGA-II), but replacing Pareto dominance by the above non-outranked concept. So, we search for the non-strictly outranked frontier that is a subset of the Pareto frontier. In several instances of a nine-objective knapsack problem our proposal clearly outperforms the standard NSGA-II, achieving non-outranked solutions which are in an obviously privileged zone of the Pareto frontier. (C) 2009 Elsevier Ltd. All rights reserved.
Keyword:
Multicriteria optimization
Evolutionary algorithms
Fuzzy preferences
Outranking relations
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
C
IF:
4.3
论文数:
6.5K
被引数:
1.8W
机构
引用论文
An evolutionary approach to construction of outranking models for multicriteria classification: The case of the ELECTRE TRI method一种用于构建多准则分类的排名模型的进化方法: ELECTRE TRI方法的情况
.png){kind=icon}
PREAI
分享收藏
分享收藏
查看更多内容