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...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:67e502f21881405d9ffd23c2b48352032021-11-16T07:46:39ZTeeport: Break the Wall Between the Optimization Algorithms and Problems2624-909X10.3389/fdata.2021.734650https://doaj.org/article/67e502f21881405d9ffd23c2b48352032021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdata.2021.734650/fullhttps://doaj.org/toc/2624-909XOptimization 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.Zhe ZhangXiaobiao HuangMinghao SongMinghao SongFrontiers Media S.A.articleoptimizationreal-time communicationbenchmarkingremoteplatformInformation technologyT58.5-58.64ENFrontiers in Big Data, Vol 4 (2021) |
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optimization real-time communication benchmarking remote platform Information technology T58.5-58.64 |
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optimization real-time communication benchmarking remote platform Information technology T58.5-58.64 Zhe Zhang Xiaobiao Huang Minghao Song Minghao Song Teeport: Break the Wall Between the Optimization Algorithms and Problems |
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 |
article |
author |
Zhe Zhang Xiaobiao Huang Minghao Song Minghao Song |
author_facet |
Zhe Zhang Xiaobiao Huang Minghao Song Minghao Song |
author_sort |
Zhe Zhang |
title |
Teeport: Break the Wall Between the Optimization Algorithms and Problems |
title_short |
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_sort |
teeport: break the wall between the optimization algorithms and problems |
publisher |
Frontiers Media S.A. |
publishDate |
2021 |
url |
https://doaj.org/article/67e502f21881405d9ffd23c2b4835203 |
work_keys_str_mv |
AT zhezhang teeportbreakthewallbetweentheoptimizationalgorithmsandproblems AT xiaobiaohuang teeportbreakthewallbetweentheoptimizationalgorithmsandproblems AT minghaosong teeportbreakthewallbetweentheoptimizationalgorithmsandproblems AT minghaosong teeportbreakthewallbetweentheoptimizationalgorithmsandproblems |
_version_ |
1718426607818899456 |