Nonlinear optimization for a low-emittance storage ring

A multi-objective genetic algorithm (MOGA) is a powerful global optimization tool, but its results are considerably affected by the crossover parameter ηc. Finding an appropriate ηc demands too much computing time because MOGA needs be run several times in order to find a good ηc. In this paper, a s...

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التفاصيل البيبلوغرافية
الحاوية / القاعدة:Journal of Synchrotron Radiation
المؤلفون الرئيسيون: Bonghoon Oh, Jinjoo Ko, Seunghwan Shin, Jaehyun Kim, Jaeyu Lee, Gyeongsu Jang
التنسيق: مقال
اللغة:الإنجليزية
منشور في: International Union of Crystallography 2024-07-01
الموضوعات:
storage ring
dynamic aperture
moga
mggop
self-adaptive crossover
الوصول للمادة أونلاين:https://journals.iucr.org/paper?S1600577524004569
الوصف
الملخص:A multi-objective genetic algorithm (MOGA) is a powerful global optimization tool, but its results are considerably affected by the crossover parameter ηc. Finding an appropriate ηc demands too much computing time because MOGA needs be run several times in order to find a good ηc. In this paper, a self-adaptive crossover parameter is introduced in a strategy to adopt a new ηc for every generation while running MOGA. This new scheme has also been adopted for a multi-generation Gaussian process optimization (MGGPO) when producing trial solutions. Compared with the existing MGGPO and MOGA, the MGGPO and MOGA with the new strategy show better performance in nonlinear optimization for the design of low-emittance storage rings.
تدمد:1600-5775

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