IPAC2018 - List of Authors (Henderson, G.)

Paper	Title	Page
WEPAF054	Online Multi Objective Optimisation at Diamond Light Source	1944
	M. Apollonio, R. Bartolini, R.T. Fielder, I.P.S. Martin DLS, Oxfordshire, United Kingdom R. Bartolini JAI, Oxford, United Kingdom G. Henderson Oxford University, Physics Department, Oxford, Oxon, United Kingdom J. Rogers Imperial College of Science and Technology, Department of Physics, London, United Kingdom
	At Diamond Light Source we have developed an Optimization Package currently used online to improve the performance of the machine, usually measured in terms of lifetime, injection efficiency or beam disturbance at injection. The tool is flexible in that control variables in order to optimise objectives (or their functions) can be easily specified by means of EPICS process variables (PV), making it suitable for virtually any sort of optimization. At present three different algorithms can be used to perform optimizations in a multi-objective fashion: Multi-Objective Genetic Algorithm (MOGA), Particle Swarm Optimizer (MOPSO) and Simulated Annealing (MOSA). We present a series of tests aimed at characterizing the algorithm as well as improving the performance of the machine itself.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF054
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Paper

Title

Page

Online Multi Objective Optimisation at Diamond Light Source

1944

M. Apollonio, R. Bartolini, R.T. Fielder, I.P.S. Martin
DLS, Oxfordshire, United Kingdom
R. Bartolini
JAI, Oxford, United Kingdom
G. Henderson
Oxford University, Physics Department, Oxford, Oxon, United Kingdom
J. Rogers
Imperial College of Science and Technology, Department of Physics, London, United Kingdom

At Diamond Light Source we have developed an Optimization Package currently used online to improve the performance of the machine, usually measured in terms of lifetime, injection efficiency or beam disturbance at injection. The tool is flexible in that control variables in order to optimise objectives (or their functions) can be easily specified by means of EPICS process variables (PV), making it suitable for virtually any sort of optimization. At present three different algorithms can be used to perform optimizations in a multi-objective fashion: Multi-Objective Genetic Algorithm (MOGA), Particle Swarm Optimizer (MOPSO) and Simulated Annealing (MOSA). We present a series of tests aimed at characterizing the algorithm as well as improving the performance of the machine itself.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAF054

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)