Parallel general purpose multiobjective optimization framework with application to electron beam dynamics

Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a nontrivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R&D. Individual experiments often have challenging beam requirements, and the physical configuration of the beam lines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beam line (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multiobjective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a generalpurpose framework for simulation-based multiobjective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and OPAL as the forward solver, validation experiments and results of multiobjective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beam line found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.

[1]  El-Ghazali Talbi,et al.  ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization , 2007, EMO.

[2]  David Robin,et al.  Global optimization of an accelerator lattice using multiobjective genetic algorithms , 2009 .

[3]  Rajeev Thakur,et al.  Toward message passing for a million processes: characterizing MPI on a massive scale blue gene/P , 2009, Computer Science - Research and Development.

[4]  A. Adelmann,et al.  THE OBJECT ORIENTED PARALLEL ACCELERATOR LIBRARY (OPAL), DESIGN, IMPLEMENTATION AND APPLICATION , 2010 .

[5]  Charles Sinclair,et al.  Multivariate optimization of a high brightness dc gun photoinjector , 2005 .

[6]  C. Gong A novel optimization platform and its applications to the TRIUMF energy recovery linac , 2015 .

[7]  Gara Miranda,et al.  Metco: a Parallel Plugin-Based Framework for Multi-Objective Optimization , 2009, Int. J. Artif. Intell. Tools.

[8]  Francisco Luna,et al.  jMetal: a Java Framework for Developing Multi-Objective Optimization Metaheuristics , 2006 .

[9]  Hamed Shah-Hosseini,et al.  The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm , 2009, Int. J. Bio Inspired Comput..

[10]  Bogdan Filipič,et al.  Parallel Evolutionary Computation Framework for Single- and Multiobjective Optimization , 2009 .

[11]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[12]  W. Gai,et al.  Short-pulse dielectric two-beam acceleration , 2012, Journal of Plasma Physics.

[13]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[14]  Terry Clark,et al.  Parallel Computing , 2017, Encyclopedia of GIS.

[15]  Lucas Bradstreet,et al.  A Fast Way of Calculating Exact Hypervolumes , 2012, IEEE Transactions on Evolutionary Computation.

[16]  F. Azuaje Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[17]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[19]  M. Borland,et al.  Elegant : a flexible SDDS-compliant code for accelerator simulation. , 2000 .

[20]  Anand D. Sarwate,et al.  Broadcast Gossip Algorithms for Consensus , 2009, IEEE Transactions on Signal Processing.

[21]  Colwyn Gulliford,et al.  Multiobjective optimization design of an rf gun based electron diffraction beam line , 2017 .

[22]  P. Arbenz,et al.  Multi-objective shape optimization of radio frequency cavities using an evolutionary algorithm , 2018, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.

[23]  W. Gai,et al.  Electron acceleration through two successive electron beam driven wakefield acceleration stages , 2018 .

[24]  W. Gai,et al.  Design and testing of a 7.8 GHz power extractor using a cylindrical dielectric-loaded waveguide , 2008 .

[25]  Yves Roblin,et al.  Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics , 2013 .

[26]  Xiaobiao Huang,et al.  Robust simplex algorithm for online optimization , 2018, Physical Review Accelerators and Beams.

[27]  Stephen P. Boyd,et al.  Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.

[28]  Desh Ranjan,et al.  Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm , 2014 .

[29]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[30]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[31]  J. van Leeuwen,et al.  Evolutionary Multi-Criterion Optimization , 2003, Lecture Notes in Computer Science.

[32]  C. Gulliford,et al.  Multiobjective optimizations of a novel cryocooled dc gun based ultrafast electron diffraction beam line , 2016 .

[33]  Peter Arbenz,et al.  On solving complex-symmetric eigenvalue problems arising in the design of axisymmetric VCSEL devices , 2008 .

[34]  R. Lyndon While,et al.  A Scalable Multi-objective Test Problem Toolkit , 2005, EMO.