Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization prob...

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Bibliographic Details
Main Authors: Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
reinforcement learning
Bayesian optimization
genetic algorithm
multi objective
MORL
MOGA
Online Access:https://doi.org/10.1088/2632-2153/adc221
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https://doi.org/10.1088/2632-2153/adc221

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