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YARI KÜRESEL SAPTIRICI ENERJİ ANALİZÖRLERİ İÇİN GENETİK ALGORİTMA İLE EN İYİ VOLTAJ PROFİLİ

Year 2018, Volume: 4 Issue: 1, 74 - 78, 27.06.2018
https://doi.org/10.22531/muglajsci.422389

Abstract

Bu çalışmada, yüklü parçacıkların enerjisine
göre analizini gerçekleştirebilen, yarı küresel saptırıcı analizörünü optimize
etmek için genetik algoritma (GA) yöntemi kullanılmıştır. Evrim kodlarından
esinlenen GA, bireyler popülasyonundan oluşmaktadır. Her adımda, GA,  popülasyon bireylerini ebeveyn olarak seçip
bu bireyleri gelecek nesil için çocukları üretmek için kullanır. Böylece
kuşaklar boyunca, nüfus en iyi bir çözüme doğru gelişmektedir. Bu çalışmanın
amacı, enerji analizörüne ait voltaj denklemleri aracılığıyla, 180˚ yarı küresel
enerji analizörleri için en iyi voltaj profilini elde etmektir. Deneysel
çalışmalarda en çok kullanılan analizör olan 180˚ yarı küresel saptırıcı
analizörü için en iyi voltaj değerleri yüksek doğrulukla bu çalışmada bulunmuştur.
GA ile analizör sistemi gibi karmaşık bir elektron optiksel sistemin
optimizasyonu, voltaj denklemleri kullanılarak elde edilmiştir ve problemin
zorluğu göz önüne alındığında oldukça iyi çalıştığı bulunmuştur. Literatürde
yer alan diğer tekniklerin aksine, önerilen GA yöntemi aracılığıyla, en iyi
voltaj profili kısa sürede elde edilmektedir.

References

  • Imhof R. E., Adams A., King G. C., “Energy and Time Resolution of the 180 Degrees Hemispherical Electrostatic Analyzer”, Journal of Physics E: Scientific Instruments, Vol. 9, No.2, pp. 138-142. 1976.
  • Heddle D.W.O., Electrostatic Lens Systems, CRC Press, United Kingdom. 2000.
  • Sise O., Ulu M., Dogan M., Martinez G., Zouros T. J. M., “Fringing Field Optimization of Hemispherical Deflector Analyzers Using BEM and FDM”, J. Elect. Spect. Rel. Phen., Vol. 177, No. 1, pp. 42-51, 2010.
  • R. Herzog, “Ablenkung von Kathoden- und Kanalstrahlen am Rande eines Kondensators, dessen Streufeld durch eine Blende begrenzt ist”, Z. Physik, Vol. 97, pp. 596-602, 1935.
  • K. Jost, “Fringing field correction for 127 degrees and 180 degrees electron spectrometers”, J. Phys. E: Sci. Instrum., Vol. 12, pp. 1001, 1979.
  • N. Martensson, “A Very High Resolution Electron Spectrometer”, J. Electron Spectrosc. Relat. Phenom., Vol. 70, pp. 117-128, 1994.
  • E.P. Benis, T.J.M. Zouros, “Improving the Energy Resolution of a Hemispherical Spectrograph Using a Paracentric Entry at a Non-zero Potential”, Nucl. Instrum. Meth. Phys. Res. A, Vol. 440, pp. 462-465, 2000.
  • T.J.M. Zouros, E.P. Benis, “The Hemispherical Deflector Analyser Revisited. I. Motion in the Ideal 1/r Potential, Generalized Entry Conditions, Kepler Orbits and Spectrometer Basic Equation”, J. Electron Spectrosc. Relat. Phenom. Vol. 125, pp. 221–248, ibid. Vol. 142 (2005) 175–176, 2002.
  • T.J.M. Zouros, O. Sise, M. Ulu, M. Dogan, “DESIGN NOTE: Using the fringing fields of a hemispherical spectrograph to improve its energy resolution”, Meas. Sci. Technol. 17, 2006, pp. N81–N86, 2006.
  • O. Sise, T.J.M. Zouros, M. Ulu, M. Dogan, “Novel and traditional fringing field correction schemes for the hemispherical analyser: Comparison of first-order focusing and energy resolution”, Meas. Sci. Technol., Vol. 18, pp.1853-1858, 2007. [11]
  • B.P. Benis, T.J.M. Zouros, “The Hemispherical Deflector Analyser Revisited II. Electron-Optical Properties”,J. Electron Spectrosc. Relat. Phenom., Vol. 163, pp.28–39, 2008.
  • Sise O., Zouros T. J. M., Position, Energy, and Transit Time Distributions in a Hemispherical Deflector Analyzer with Position Sensitive Detector, J. Spect., Vol. 1535, pp. 13-20, 2015.
  • Işık N., Işık A.H., Sise O., Guvenc U., “Prediction of First Order Focusing Properties of Ideal Hemispherical Deflector Analyzer Using Artificial Neural Network” Vol. 131, No. 1, pp. 10-12, 2017.
  • Goldberg D. E., Holland, J. H., “Genetic Algorithms and Machine Learning”, Mach Learn., Vol. 3, No.2, pp. 95-99, 1988.
  • Bashir L. Z., “Solve Simple Linear Equation using Evolutionary Algorithm”, World Sci. News, Vol.19, pp. 148-167, 2015.
  • Bashir L. Z., Mahdi, N., “Use Genetic Algorithm in Optimization Function for Solving Queens Problem”. World Sci. News, Vol. 11, pp.138-150, 2015.
  • Ahmadi M. H., Ahmadi M. A., , “Thermodynamic Analysis and Optimisation of an Irreversible Radiative-Type Heat Engine by Using Non-dominated Sorting Genetic Algorithm, Int. J. Ambient Energy, Vol. 37, No. 4, pp. 403-408, 2016.
  • Deaven D. M., Tit N., Morris J. R., Ho K. M., “Structural optimization of Lennard-Jones Clusters by a Genetic Algorithm”, Chem. Phys. Lett., Vol. 256, No. 1-2, pp. 195-200, 1996.
  • Zhang L., Wang L., Hinds G., Lyu C., Zheng J., Li J., “Multi-Objective Optimization of Lithium-Ion Battery Model Using Genetic Algorithm Approach”, J. Power Sources, Vol. 270, pp. 367-378, 2014.
  • Deaven D. M., Ho K. M., “Molecular Geometry Optimization with a Genetic Algorithm”, Phys. Rev. Let., Vol. 75, No. 2, pp.288, 1995.
  • Goldberg D.E., “Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Press, Boston, USA, 1989.
  • Jang J.S.R., “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Chapter 7: Derivative-Free Optimization”, Prentice-Hall Press, USA, 1997.
  • Wang Y. Z., “Using Genetic Algorithm Methods to Solve Course Scheduling Problems”, Expert Syst. Appl., Vol. 25, No. 1, pp. 39-50, 2003.
  • Paes F. G., Pessoa A. A., Vidal T., “A Hybrid Genetic Algorithm with Decomposition Phases for the Unequal Area Facility Layout Problem”, Eur. J. Oper. Res., Vol. 256, No.3, pp. 742-756, 2017.
  • Raeisi-Vanani H., Shayannejad M., Soltani-Toudeshki A. R., Arab M. A., Eslamian S., Amoushahi-Khouzani M., Ostad-Ali-Askari K., “A Simple Method for Land Grading Computations and its Comparison with Genetic Algorithm (GA) Method”, Int. Res. Stud. Agric Sci., Vol. 3, No. 8, pp. 26-38, 2017.
  • Keshanchi B., Souri A., Navimipour N. J., “An Improved Genetic Algorithm for Task Scheduling in the Cloud Environments Using the Priority Queues: Formal Verification, Simulation, and Statistical Testing”, J. Syst. Softw., Vol. 124, pp. 1-21, 2017.
  • Yuan X., Elhoseny M., El-Minir H. K., Riad A. M., “A Genetic Algorithm-Based, Dynamic Clustering Method Towards Improved WSN Longevity”, J. Netw. Syst. Manag., Vol. 25, No.1, pp. 21-46, 2017.
  • Ahuja H., Batra U., “Innovations in Computational Intelligence”, Springer Press, Singapore, 2018.
  • Syahputra R., “Distribution Network Optimization Based on Genetic Algorithm”, J. Electr. Technol., Vol. 1, No.1, pp.1-9, 2017.
  • Coley D., “An Introduction to Genetic Algorithm for Scientist and Engineers”. World Scientific Publishing, USA, 1999.

OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM

Year 2018, Volume: 4 Issue: 1, 74 - 78, 27.06.2018
https://doi.org/10.22531/muglajsci.422389

Abstract

In this study, the genetic algorithm (GA) method
is used to optimize the hemispherical deflector analyzer, which can analyze
according to the energies of charged particles. The GA inspired by evolutionary
codes consists of the population of individuals. At each step, the GA, selects
the population individuals as parents and uses them to produce children for the
next generation. Thus throughout generations, the population evolves to an
optimal solution. The purpose of this study is to achieve the best voltage
profile for the 180° hemispherical energy analyzers through the voltage
equations of the energy analyzer. The best voltage values ​​for the 180˚
hemispherical deflector analyzer, which is the most used analyzer in
experimental studies, are found with high accuracy in this study. Optimization
of a complex electron optical system, such as an analyzer system with a GA, has
been achieved using voltage equations and has been found to work very well
given the difficulty of the problem. In contrast to other techniques in the
literature, the best voltage profile is obtained in a short time by means of
the proposed GA method.

References

  • Imhof R. E., Adams A., King G. C., “Energy and Time Resolution of the 180 Degrees Hemispherical Electrostatic Analyzer”, Journal of Physics E: Scientific Instruments, Vol. 9, No.2, pp. 138-142. 1976.
  • Heddle D.W.O., Electrostatic Lens Systems, CRC Press, United Kingdom. 2000.
  • Sise O., Ulu M., Dogan M., Martinez G., Zouros T. J. M., “Fringing Field Optimization of Hemispherical Deflector Analyzers Using BEM and FDM”, J. Elect. Spect. Rel. Phen., Vol. 177, No. 1, pp. 42-51, 2010.
  • R. Herzog, “Ablenkung von Kathoden- und Kanalstrahlen am Rande eines Kondensators, dessen Streufeld durch eine Blende begrenzt ist”, Z. Physik, Vol. 97, pp. 596-602, 1935.
  • K. Jost, “Fringing field correction for 127 degrees and 180 degrees electron spectrometers”, J. Phys. E: Sci. Instrum., Vol. 12, pp. 1001, 1979.
  • N. Martensson, “A Very High Resolution Electron Spectrometer”, J. Electron Spectrosc. Relat. Phenom., Vol. 70, pp. 117-128, 1994.
  • E.P. Benis, T.J.M. Zouros, “Improving the Energy Resolution of a Hemispherical Spectrograph Using a Paracentric Entry at a Non-zero Potential”, Nucl. Instrum. Meth. Phys. Res. A, Vol. 440, pp. 462-465, 2000.
  • T.J.M. Zouros, E.P. Benis, “The Hemispherical Deflector Analyser Revisited. I. Motion in the Ideal 1/r Potential, Generalized Entry Conditions, Kepler Orbits and Spectrometer Basic Equation”, J. Electron Spectrosc. Relat. Phenom. Vol. 125, pp. 221–248, ibid. Vol. 142 (2005) 175–176, 2002.
  • T.J.M. Zouros, O. Sise, M. Ulu, M. Dogan, “DESIGN NOTE: Using the fringing fields of a hemispherical spectrograph to improve its energy resolution”, Meas. Sci. Technol. 17, 2006, pp. N81–N86, 2006.
  • O. Sise, T.J.M. Zouros, M. Ulu, M. Dogan, “Novel and traditional fringing field correction schemes for the hemispherical analyser: Comparison of first-order focusing and energy resolution”, Meas. Sci. Technol., Vol. 18, pp.1853-1858, 2007. [11]
  • B.P. Benis, T.J.M. Zouros, “The Hemispherical Deflector Analyser Revisited II. Electron-Optical Properties”,J. Electron Spectrosc. Relat. Phenom., Vol. 163, pp.28–39, 2008.
  • Sise O., Zouros T. J. M., Position, Energy, and Transit Time Distributions in a Hemispherical Deflector Analyzer with Position Sensitive Detector, J. Spect., Vol. 1535, pp. 13-20, 2015.
  • Işık N., Işık A.H., Sise O., Guvenc U., “Prediction of First Order Focusing Properties of Ideal Hemispherical Deflector Analyzer Using Artificial Neural Network” Vol. 131, No. 1, pp. 10-12, 2017.
  • Goldberg D. E., Holland, J. H., “Genetic Algorithms and Machine Learning”, Mach Learn., Vol. 3, No.2, pp. 95-99, 1988.
  • Bashir L. Z., “Solve Simple Linear Equation using Evolutionary Algorithm”, World Sci. News, Vol.19, pp. 148-167, 2015.
  • Bashir L. Z., Mahdi, N., “Use Genetic Algorithm in Optimization Function for Solving Queens Problem”. World Sci. News, Vol. 11, pp.138-150, 2015.
  • Ahmadi M. H., Ahmadi M. A., , “Thermodynamic Analysis and Optimisation of an Irreversible Radiative-Type Heat Engine by Using Non-dominated Sorting Genetic Algorithm, Int. J. Ambient Energy, Vol. 37, No. 4, pp. 403-408, 2016.
  • Deaven D. M., Tit N., Morris J. R., Ho K. M., “Structural optimization of Lennard-Jones Clusters by a Genetic Algorithm”, Chem. Phys. Lett., Vol. 256, No. 1-2, pp. 195-200, 1996.
  • Zhang L., Wang L., Hinds G., Lyu C., Zheng J., Li J., “Multi-Objective Optimization of Lithium-Ion Battery Model Using Genetic Algorithm Approach”, J. Power Sources, Vol. 270, pp. 367-378, 2014.
  • Deaven D. M., Ho K. M., “Molecular Geometry Optimization with a Genetic Algorithm”, Phys. Rev. Let., Vol. 75, No. 2, pp.288, 1995.
  • Goldberg D.E., “Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Press, Boston, USA, 1989.
  • Jang J.S.R., “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Chapter 7: Derivative-Free Optimization”, Prentice-Hall Press, USA, 1997.
  • Wang Y. Z., “Using Genetic Algorithm Methods to Solve Course Scheduling Problems”, Expert Syst. Appl., Vol. 25, No. 1, pp. 39-50, 2003.
  • Paes F. G., Pessoa A. A., Vidal T., “A Hybrid Genetic Algorithm with Decomposition Phases for the Unequal Area Facility Layout Problem”, Eur. J. Oper. Res., Vol. 256, No.3, pp. 742-756, 2017.
  • Raeisi-Vanani H., Shayannejad M., Soltani-Toudeshki A. R., Arab M. A., Eslamian S., Amoushahi-Khouzani M., Ostad-Ali-Askari K., “A Simple Method for Land Grading Computations and its Comparison with Genetic Algorithm (GA) Method”, Int. Res. Stud. Agric Sci., Vol. 3, No. 8, pp. 26-38, 2017.
  • Keshanchi B., Souri A., Navimipour N. J., “An Improved Genetic Algorithm for Task Scheduling in the Cloud Environments Using the Priority Queues: Formal Verification, Simulation, and Statistical Testing”, J. Syst. Softw., Vol. 124, pp. 1-21, 2017.
  • Yuan X., Elhoseny M., El-Minir H. K., Riad A. M., “A Genetic Algorithm-Based, Dynamic Clustering Method Towards Improved WSN Longevity”, J. Netw. Syst. Manag., Vol. 25, No.1, pp. 21-46, 2017.
  • Ahuja H., Batra U., “Innovations in Computational Intelligence”, Springer Press, Singapore, 2018.
  • Syahputra R., “Distribution Network Optimization Based on Genetic Algorithm”, J. Electr. Technol., Vol. 1, No.1, pp.1-9, 2017.
  • Coley D., “An Introduction to Genetic Algorithm for Scientist and Engineers”. World Scientific Publishing, USA, 1999.
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Journals
Authors

Nimet Işık 0000-0002-1347-6628

Publication Date June 27, 2018
Published in Issue Year 2018 Volume: 4 Issue: 1

Cite

APA Işık, N. (2018). OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM. Mugla Journal of Science and Technology, 4(1), 74-78. https://doi.org/10.22531/muglajsci.422389
AMA Işık N. OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM. Mugla Journal of Science and Technology. June 2018;4(1):74-78. doi:10.22531/muglajsci.422389
Chicago Işık, Nimet. “OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM”. Mugla Journal of Science and Technology 4, no. 1 (June 2018): 74-78. https://doi.org/10.22531/muglajsci.422389.
EndNote Işık N (June 1, 2018) OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM. Mugla Journal of Science and Technology 4 1 74–78.
IEEE N. Işık, “OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM”, Mugla Journal of Science and Technology, vol. 4, no. 1, pp. 74–78, 2018, doi: 10.22531/muglajsci.422389.
ISNAD Işık, Nimet. “OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM”. Mugla Journal of Science and Technology 4/1 (June 2018), 74-78. https://doi.org/10.22531/muglajsci.422389.
JAMA Işık N. OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM. Mugla Journal of Science and Technology. 2018;4:74–78.
MLA Işık, Nimet. “OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM”. Mugla Journal of Science and Technology, vol. 4, no. 1, 2018, pp. 74-78, doi:10.22531/muglajsci.422389.
Vancouver Işık N. OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM. Mugla Journal of Science and Technology. 2018;4(1):74-8.

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