并行通用多目标优化框架及其在梁动力学中的应用
A Parallel General Purpose Multi-Objective Optimization Framework, with Application to Beam Dynamics
摘要：
粒子加速器是基础科学和应用科学研究的宝贵工具，涉及材料科学、化学、生物科学、粒子物理、核物理和医学等领域。加速器设施的设计、调试和运行是一项重要的任务，这是由于大量的控制参数和几个相互冲突的设计目标之间的复杂相互作用。我们建议通过多目标优化算法来解决这个问题，这也有利于并行部署。为了在有意义的时间框架内计算解决方案，需要快速和可扩展的软件框架。在本文中，我们提出了这样一个通用的框架，基于仿真的多目标优化方法，允许机器参数的最佳设置的自动调查的实施。该实现是基于主/从范式，采用几个主机，管理一组从执行模拟和执行优化任务。采用进化算法作为优化器，OPAL作为正向求解器，验证实验和多目标优化问题的束流动力学域的结果。束流动力学模型采用阿贡尾波场加速器的高电荷束流线。优化了3D光束尺寸、横向动量和能量扩散。
英文摘要：
Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame a fast and scalable software framework is required. In this paper, we present the implementation of such a general-purpose framework for simulation-based multi-objective optimization methods that allows the automatic investigation of optimal sets of machine parameters. The implementation is based on a master/slave paradigm, employing several masters that govern a set of slaves executing simulations and performing optimization tasks. Using evolutionary algorithms as the optimizer and OPAL as the forward solver, validation experiments and results of multi-objective optimization problems in the domain of beam dynamics are presented. The high charge beam line at the Argonne Wakefield Accelerator Facility was used as the beam dynamics model. The 3D beam size, transverse momentum, and energy spread were optimized.
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