Yıl 2018, Cilt 4, Sayı 1, Sayfalar 74 - 78 2018-06-26

OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM
YARI KÜRESEL SAPTIRICI ENERJİ ANALİZÖRLERİ İÇİN GENETİK ALGORİTMA İLE EN İYİ VOLTAJ PROFİLİ

Nimet Işık [1]

25 34

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.

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.

  • 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.
Birincil Dil en
Konular Mühendislik ve Temel Bilimler
Dergi Bölümü Makaleler
Yazarlar

Orcid: 0000-0002-1347-6628
Yazar: Nimet Işık (Sorumlu Yazar)
Kurum: Mehmet Akif Ersoy Üniversitesi
Ülke: Turkey


Bibtex @araştırma makalesi { muglajsci422389, journal = {Mugla Journal of Science and Technology}, issn = {2149-3596}, address = {Mugla Sitki Kocman University}, year = {2018}, volume = {4}, pages = {74 - 78}, doi = {10.22531/muglajsci.422389}, title = {OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM}, key = {cite}, author = {Işık, Nimet} }
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. DOI: 10.22531/muglajsci.422389
MLA Işık, N . "OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM". Mugla Journal of Science and Technology 4 (2018): 74-78 <http://dergipark.gov.tr/muglajsci/issue/37235/422389>
Chicago Işık, N . "OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM". Mugla Journal of Science and Technology 4 (2018): 74-78
RIS TY - JOUR T1 - OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM AU - Nimet Işık Y1 - 2018 PY - 2018 N1 - doi: 10.22531/muglajsci.422389 DO - 10.22531/muglajsci.422389 T2 - Mugla Journal of Science and Technology JF - Journal JO - JOR SP - 74 EP - 78 VL - 4 IS - 1 SN - 2149-3596- M3 - doi: 10.22531/muglajsci.422389 UR - http://dx.doi.org/10.22531/muglajsci.422389 Y2 - 2018 ER -
EndNote %0 Mugla Journal of Science and Technology OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM %A Nimet Işık %T OPTIMAL VOLTAGE PROFILE FOR HEMISPHERICAL DEFLECTOR ENERGY ANALYZERS USING GENETIC ALGORITHM %D 2018 %J Mugla Journal of Science and Technology %P 2149-3596- %V 4 %N 1 %R doi: 10.22531/muglajsci.422389 %U 10.22531/muglajsci.422389
ISNAD Işık, Nimet . "YARI KÜRESEL SAPTIRICI ENERJİ ANALİZÖRLERİ İÇİN GENETİK ALGORİTMA İLE EN İYİ VOLTAJ PROFİLİ". Mugla Journal of Science and Technology 4 / 1 (Haziran 2018): 74-78. http://dx.doi.org/10.22531/muglajsci.422389