基于贝叶斯框架的两阶段随机模型更新方法和重载铁路桥梁的混合多种群迁移遗传-大都会-哈斯廷斯算法

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2024-05-29 DOI:10.1080/0305215x.2024.2351197

Hongyin Yang, Nanhao Wu, Hongyou Cao, Wei Zhang

引用次数: 0

摘要

本研究在贝叶斯推断的基础上，提出了一种使用改进的多种群迁移遗传算法（MMGA）和Metropolis-Hastings（MH）算法的随机模型更新方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Two-stage stochastic model updating approach based on Bayesian framework and hybrid multi-population migrant genetic–Metropolis–Hastings algorithm for heavy-haul railway bridges

This study proposes a stochastic model updating approach using an improved multi-population migrant genetic algorithm (MMGA) with the Metropolis–Hastings (MH) algorithm based on the Bayesian infere...

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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.