.png){kind=icon}
返回
GBOS: Generalized Best Order Sort algorithm for non-dominated sorting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2018.06.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Non-dominated sorting is one of the prominent steps in developing any Pareto-dominance based multi-objective evolutionary algorithm. The computational complexity of any Pareto-dominance based multi-objective evolutionary algorithm primarily depends on this step. Thus, researchers are working on reducing the complexity of this step. Recently, an efficient approach for non-dominated sorting known as Best Order Sort (BOS) has been proposed. This approach is very efficient in terms of the number of comparisons between the solutions. Another advantage of this approach is that while comparing two solutions, the number of objectives which are compared is reduced from the actual number of objectives associated with each solution. However, in spite of these two advantages, this approach is not suitable in its current form for cases in which we have duplicate solutions. This paper generalizes BOS to handle duplicate solutions while retaining both of its advantages. We call this generalized version Generalized Best Order Sort (GBOS). The present work shows that BOS can be generalized to handle its limitation without compromising its time and space complexity.
Keyword:
Non-dominated sorting
Dominance relation
Computational complexity
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:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization一种基于决策变量聚类的大规模多目标优化进化算法
OAAI
OAAI分享收藏分享收藏
查看更多内容