Detail publikace

Reliability-based Optimization: Optimizatio Strategy for Small-Sample Analysis

Originální název

Reliability-based Optimization: Optimizatio Strategy for Small-Sample Analysis

Anglický název

Reliability-based Optimization: Optimizatio Strategy for Small-Sample Analysis

Jazyk

en

Originální abstrakt

The aim of the paper is to present a newly developed approach for reliability - based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability - based optmization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulations using an advanced Latin Hypercube Sampling technique. This model is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous computational demands.

Anglický abstrakt

The aim of the paper is to present a newly developed approach for reliability - based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability - based optmization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulations using an advanced Latin Hypercube Sampling technique. This model is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous computational demands.

BibTex


@inproceedings{BUT113717,
  author="Ondřej {Slowik} and Drahomír {Novák}",
  title="Reliability-based Optimization: Optimizatio Strategy for Small-Sample Analysis",
  annote="The aim of the paper is to present a newly developed approach for reliability - based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability - based optmization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulations using an advanced Latin Hypercube Sampling technique. This model is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous computational demands.",
  booktitle="New Trends in Statics and Dynamics of Buildings",
  chapter="113717",
  howpublished="electronic, physical medium",
  year="2014",
  month="october",
  pages="1--7",
  type="conference paper"
}