Study of solution diversity in multi-objective optimization of storage ring lattice

Study of solution diversity in multi-objective optimization of storage ring lattice
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存储环格子多目标优化解多样性研究

DOI:
10.1088/1748-0221/17/04/p04019
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发表时间:
2022
影响因子:
1.3
通讯作者:
L. Wang
L. Wang
中科院分区:
工程技术4区
文献类型:
--
作者:
J. Xu;J. Tan;G. Liu;Z. Bai;L. Wang

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进化算法,包括多目标遗传算法(莫加)和多目标粒子群优化算法(MOPSO),已被广泛应用于储存环晶格设计,在搜索最优目标函数值(即Pareto前沿)方面表现出良好的性能。在数学上,不同的变量值可以具有相同的目标函数值,这称为多对一映射。与以往研究Pareto前沿收敛性的方法不同,本文研究了存储环格点优化中变量的多样性问题,并对莫加和MOPSO进行了比较。以两种不同的格为研究对象。研究表明,在目标几乎相同、变量不同的情况下,储存环的晶格解在某些性质上会表现出差异,这对晶格的选择是有利的。此外,与MOPSO算法相比,莫加算法虽然能得到几乎相同的Pareto前沿,但其最优格点解在变量空间的分布范围更广。
Evolutionary algorithms, including multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), have been widely used for storage ring lattice designs, showing good performance in searching the optimal objective function values (i.e. Pareto front). Mathematically, different variable values can have the same objective function value, which is called many-to-one mapping. Different from focusing on the convergence of Pareto front, in this paper we study the diversity of variables in the optimization of storage ring lattice and make a comparison between MOGA and MOPSO. Two different lattices are taken as study examples. The study shows that the lattice solutions with almost the same objectives and different variables can show difference in some storage ring properties, which is beneficial for lattice selection. Besides, compared to MOPSO, MOGA gives a wider distribution of optimal lattice solutions in the variable space, though both algorithms can obtain almost the same Pareto front.
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