Global optimization of an accelerator lattice using multiobjective genetic algorithms
Global optimization of an accelerator lattice using multiobjective genetic algorithms
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DOI：
10.1016/j.nima.2009.08.027
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发表时间：
2009-10
影响因子：
1.4
通讯作者：
Lingyun Yang;D. Robin;F. Sannibale;C. Steier;W. Wan
中科院分区：
文献类型：
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作者：
Lingyun Yang;D. Robin;F. Sannibale;C. Steier;W. Wan
储存环晶格设计是一个高约束多目标优化问题。目标可以包括晶格函数或导出的量，如发射度、亮度或光度，同时满足约束条件，如晶格的线性稳定性。在本文中，我们将探讨使用多目标遗传算法（莫加）找到全局优化的晶格设置在存储环。以先进光源（ALS）为例，分析了莫加的三个应用实例：（1）利用三个拟合参数优化直线段电子感应加速器函数和自然发射度;（ii）使用三个拟合参数来优化晶格中的弯曲磁体和插入装置源点的光子亮度，以及（iii）六参数拟合在随后的直线段中创建交替的高和低水平电子感应加速器功能，同时仍然最小化自然发射度。利用莫加的主要优点之一，我们还研究了优化目标之间的权衡最优解集。
Storage ring lattice design is a highly constrained multiobjective optimization problem. The objectives can include lattice functions or derived quantities like emittance, brightness, or luminosity while simultaneously fulfilling constraints such as linear stability of the lattice. In this paper we explore the use of multiobjective genetic algorithms (MOGA) to find globally optimized lattice settings in a storage ring. Using the Advanced Light Source (ALS) for illustration, three examples of MOGA are shown and analyzed—(i) using three fit parameters to optimize the straight section betatron function and the natural emittance, (ii) using three fit parameters to optimize the photon brightness of bending magnet and insertion device source points in the lattice and (iii) a six parameter fit creating alternating high and low horizontal betatron functions in subsequent straight sections while still minimizing the natural emittance. Making use of one of the main benefits of MOGA, we also study the trade-offs in the optimization objectives between sets of optimal solutions.