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@inproceedings{zhang:ipac2021-mopab182,
author = {X. Zhang and E. Benedetto and S.L. Sheehy},
title = {{Automated Synchrotron Lattice Design and Optimisation Using a Multi-Objective Genetic Algorithm}},
booktitle = {Proc. IPAC'21},
pages = {616--619},
eid = {MOPAB182},
language = {english},
keywords = {network, lattice, synchrotron, dipole, superconducting-magnet},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-MOPAB182},
url = {https://jacow.org/ipac2021/papers/mopab182.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB182},
abstract = {{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.}},
}