Speaker
Description
Reducing the longitudinal variation of third-order resonance driving terms (RDTs) is much more effective in enlarging storage ring dynamic aperture (DA) than minimizing third-order one-turn RDTs. Recently, we proved the convexity of the quantitative expression for third-order RDT variations. Then, an efficient numerical method for DA optimization was developed, where a high-quality initial population for an intelligent algorithm is generated with a Gaussian distribution based on this convexity. In this paper, we study the impact of the variance of the Gaussian distribution on the optimization performance of this method. It is found that the method shows good optimization performance for small variances, and that the performance remains robust even at a very small variance. In addition, different intelligent algorithms perform well with a small Gaussian variance.
| Paper status | Resubmitted proceeding files received and assigned to an editor. Accepted by Submitter. |
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