Author: Zhang, X.
Paper Title Page
MOPAB182 Automated Synchrotron Lattice Design and Optimisation Using a Multi-Objective Genetic Algorithm 616
 
  • X. Zhang, S.L. Sheehy
    The University of Melbourne, Melbourne, Victoria, Australia
  • E. Benedetto
    TERA, Novara, Italy
  • E. Benedetto
    CERN, Meyrin, Switzerland
 
  Funding: This work is partially supported by the Australian Government Research Training Program Scholarship.
As part of the Next Ion Med­ical Ma­chine Study (NIMMS), we pre­sent a new method for de­sign­ing syn­chro­tron lat­tices. A step-wise ap­proach was used to gen­er­ate ran­dom lat­tice struc­tures from a set of feed­for­ward neural net­works. These lat­tice de­signs are op­ti­mised by evolv­ing the net­works over many it­er­a­tions with a multi-ob­jec­tive ge­netic al­go­rithm (MOGA). The final set of so­lu­tions rep­re­sent the most effi- cient and fea­si­ble lat­tices which sat­isfy the de­sign con­straints. It is up to the lat­tice de­signer to choose a de­sign that best suits the in­tended ap­pli­ca­tion. The au­to­mated al­go­rithm pre­sented here ran­domly sam­ples from all pos­si­ble lat­tice lay­outs and reaches the global op­ti­mum over many it­er­a­tions. The re­quire­ments of an ef­fi­cient ex­trac­tion scheme in hadron ther­apy syn­chro­trons im­pose strin­gent con­straints on the lat- tice op­ti­cal func­tions. Using this al­go­rithm al­lows us to find the global op­ti­mum that is tai­lored to these con­straints and to fully utilise the flex­i­bil­i­ties pro­vided by new tech­nol­ogy.
 
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DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB182  
About • paper received ※ 15 May 2021       paper accepted ※ 23 June 2021       issue date ※ 14 August 2021  
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