通过自适应代理模型加速基于抽样的容差成本优化

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2024-03-14 DOI:10.1080/0305215x.2024.2306142

Martin Roth, Stephan Freitag, Michael Franz, Stefan Goetz, Sandro Wartzack

引用次数: 0

摘要

要保证基于采样的容限成本优化的可靠性和最优性，大量的函数评估是不可避免的。尽管采用了不同的对策来增加函数评估次数，但仍有许多问题需要解决。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Accelerating sampling-based tolerance–cost optimization by adaptive surrogate models

High numbers of function evaluations are inevitable to guarantee the reliability and optimality of sampling-based tolerance–cost optimization. Despite using different countermeasures to increase it...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Engineering Optimization 管理科学-工程：综合

CiteScore

5.90

自引率

7.40%

发文量

审稿时长

3.5 months

期刊介绍： Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process. Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.