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RIS citation export for MOPAB182: Automated Synchrotron Lattice Design and Optimisation Using a Multi-Objective Genetic Algorithm

TY  - CONF
AU  - Zhang, X.
AU  - Benedetto, E.
AU  - Sheehy, S.L.
ED  - Liu, Lin
ED  - Byrd, John M.
ED  - Neuenschwander, Regis T.
ED  - Picoreti, Renan
ED  - Schaa, Volker R. W.
TI  - Automated Synchrotron Lattice Design and Optimisation Using a Multi-Objective Genetic Algorithm
J2  - Proc. of IPAC2021, Campinas, SP, Brazil, 24-28 May 2021
CY  - Campinas, SP, Brazil
T2  - International Particle Accelerator Conference
T3  - 12
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 616
EP  - 619
KW  - network
KW  - lattice
KW  - synchrotron
KW  - dipole
KW  - superconducting-magnet
DA  - 2021/08
PY  - 2021
SN  - 2673-5490
SN  - 978-3-95450-214-1
DO  - doi:10.18429/JACoW-IPAC2021-MOPAB182
UR  - https://jacow.org/ipac2021/papers/mopab182.pdf
ER  -