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Teeport: Break the Wall Between the Optimization Algorithms and Problems
Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636989/ https://www.ncbi.nlm.nih.gov/pubmed/34870190 http://dx.doi.org/10.3389/fdata.2021.734650 |
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author | Zhang, Zhe Huang, Xiaobiao Song, Minghao |
author_facet | Zhang, Zhe Huang, Xiaobiao Song, Minghao |
author_sort | Zhang, Zhe |
collection | PubMed |
description | Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed. |
format | Online Article Text |
id | pubmed-8636989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86369892021-12-03 Teeport: Break the Wall Between the Optimization Algorithms and Problems Zhang, Zhe Huang, Xiaobiao Song, Minghao Front Big Data Big Data Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed. Frontiers Media S.A. 2021-11-16 /pmc/articles/PMC8636989/ /pubmed/34870190 http://dx.doi.org/10.3389/fdata.2021.734650 Text en Copyright © 2021 Zhang, Huang and Song. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Zhang, Zhe Huang, Xiaobiao Song, Minghao Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title | Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title_full | Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title_fullStr | Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title_full_unstemmed | Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title_short | Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title_sort | teeport: break the wall between the optimization algorithms and problems |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636989/ https://www.ncbi.nlm.nih.gov/pubmed/34870190 http://dx.doi.org/10.3389/fdata.2021.734650 |
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