检索结果-内蒙古大学图书馆

Incorporating expert knowledge into evolutionary algorithms with operators and constraints to design satellite systems

引用

APPLIED SOFT COMPUTING 2018年 66卷 330-345页

作者： Hitomi, Nozomi Selva, Daniel Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

knowledge-intensive evolutionary algorithms (EA) and knowledge-driven optimization algorithms that leverage problem- or domain-specific knowledge have been shown to discover high-quality solutions with fewer function evaluations than knowledge-independent EAs. knowledge-intensive EAs apply the available knowledge through (1) knowledge-dependent operators that modify specific decision variables by biasing them towards or away from predetermined values or (2) knowledge-dependent constraints that penalize or eliminate solutions that are inconsistent with the provided knowledge. While both knowledge-dependent operators and knowledge-dependent constraints have shown promise in improving the search performance of an EA, there are no known experiments comparing their efficacy. To address the lack of such comparative experiments, this paper benchmarks one EA using knowledge-dependent operators and two EAs using knowledge-dependent constraints against an analogous knowledge-independent EA on a design problem for a climate-monitoring satellite system. Each EA is evaluated for its ability to attain high-quality solutions with the fewest possible number of function evaluations. In addition, we analyze the extent to which each method can focus on applying the knowledge that improves the search performance, handle conflicting information that suggests improving solutions with opposing modifications, and balance the exploitation of the available knowledge with the exploration of the tradespace to prevent premature convergence on local optima. The results show promise for EAs applying knowledge-dependent operators with an adaptive operator selection strategy and reveal some limitations of methods that adaptively apply knowledge-dependent constraints. (C) 2018 Elsevier B.V. All rights reserved.

关键词： knowledge-intensive evolutionary algorithms Multiobjective evolutionary algorithms Satellite system optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：