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@inproceedings{zhang:ipac2021-wepab305,
author = {Z. Zhang and X. Huang and M. Song},
title = {{Teeport: Break the Wall Between the Optimization Algorithms and Problems}},
booktitle = {Proc. IPAC'21},
pages = {3387--3390},
eid = {WEPAB305},
language = {english},
keywords = {experiment, controls, real-time, monitoring, GUI},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-WEPAB305},
url = {https://jacow.org/ipac2021/papers/wepab305.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB305},
abstract = {{Optimization algorithms/techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and Gaussian process (GP) 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, sometimes even unrealistic, since the algorithms and problems could be implemented in different languages, might require specific resources, or have physical constraints. We introduce an optimization platform named Teeport that is developed to address the above issue. This real-time communication (RTC) based platform is particularly 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.}},
}