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International Conference on Large-Scale Scientific Computing

LSSC 2017: Large-Scale Scientific Computing pp 399–407Cite as

Free Search in Multidimensional Space M

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10665))

Abstract

In the modern world of billions connected things and exponentially growing data, search in multidimensional spaces and optimisation of multidimensional tasks will become a daily need for variety of technologies and scientific fields. Resolving multidimensional tasks with thousands parameters and more require time, energy and other resources and seems to be an embarrassing challenge for modern computational systems in terms of software abilities and hardware capacity. Presented study focuses on evaluation and comparison of thousands dimensional heterogeneous real-value numerical optimisation tests on two enhanced performance computer systems. The aim is to extend the knowledge on multidimensional search and identification of acceptable solutions with non-zero probability on heterogeneous tasks. It aims also to study computational limitations, energy consumptions and time. Use of energy and time are measured and analysed. Experimental results are presented and can be used for further research and evaluation of other methods.

In Roman numeral system, letter M equals 1000, in decimal numeral system.

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References

  1. Ackley, D.H.: A Connectionist Machine for Genetic Hillclimbing. Kluwer, Boston (1987)

    Book  Google Scholar 

  2. Censor, Y.: Optimisation methods. In: Ralston, A., Reilly, E.D., Hemmendinger, D. (eds.) Encyclopedia of Computer Science, pp. 1339–1341. Nature Publishing Group, London (2000). ISBN: 0-333-77879-0

    Google Scholar 

  3. De Jung, K.A.: An analysis of the behaviour of a class of genetic adaptive systems. Ph.D. thesis, University of Michigan, USA, August 1975

    Google Scholar 

  4. Griewank, A.O.: Generalized decent for global optimization. J. Optim. Theory Appl. 34, 11–31 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  5. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1992). https://doi.org/10.1007/978-3-662-03315-9

    Book  MATH  Google Scholar 

  6. Penev, K.: Free Search of Real Value or How to Make Computers Think. St. Qu, UK (2008). ISBN 978-0-9558948-0-0

    Google Scholar 

  7. Penev, K.: Free search – comparative analysis 100. Int. J. Metaheuristics 3(2), 118–132 (2014)

    Article  Google Scholar 

  8. Penev, K.: Free search in multidimensional space II. In: Dimov, I., Fidanova, S., Lirkov, I. (eds.) NMA 2014. LNCS, vol. 8962, pp. 103–111. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15585-2_12

    Google Scholar 

  9. Rosenbrock, H.H.: An automate method for finding the greatest or least value of a function. Comput. J. 3, 175–184 (1960)

    Article  MathSciNet  Google Scholar 

  10. van der Meulen, R.: Gartner Says 8.4 Billion Connected “Things” Will Be in Use in 2017, Up 31 Percent From 2016, Gartner. http://www.gartner.com/newsroom/id/3598917. Accessed 11 July 2017

  11. Cisco: The Zettabyte Era—Trends and Analysis—Cisco, White Papers. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/vni-hyperconnectivity-wp.html. Accessed 11 July 2017

  12. Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, New York (1977). English translation of Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie

    Google Scholar 

  13. Mühlenbein, H., Schomisch, D., Born, J.: The parallel genetic algorithm as function optimizer. Parallel Comput. 17, 619–632 (1991)

    Article  MATH  Google Scholar 

  14. Raven, A.: Overclocking XEON E5 1660 V2 validation. http://valid.x86.fr/top-cpu/496e74656c2852292058656f6e285229204350552045352d31363630207632204020332e373047487a. Accessed 11 July 2017

  15. Tashkov, P.: Overclocking XEON E5 1620 V2 validation. http://valid.x86.fr/7q0py1. Accessed 11 July 2017

  16. Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)

    Article  Google Scholar 

  17. Zhongda, T., Shujiang, L., Yanhong, W., Yi, S.: A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos Solitons Fractals 98, 158–172 (2017). Elsevier

    Article  MathSciNet  MATH  Google Scholar 

  18. Sun, G., Zhao, R., Lan, Y.: Joint operations algorithm for large-scale global optimization. Appl. Soft Comput. 38, 1025–1039 (2016)

    Article  Google Scholar 

  19. Hultmann Ayala, H.V., Keller, P., De Fátima Morais, M., Mariani, V.C., Dos Santos Coelho, L., Venkata Rao, R.: Design of heat exchangers using a novel multiobjective free search differential evolution paradigm. Appl. Therm. Eng. 94, 170–177 (2016). Elsevier

    Article  Google Scholar 

  20. Hultmann Ayala, H.V., Dos Santos Coelho, L., Mariani, V.C., Askarzadeh, A.: An improved free search differential evolution algorithm: a case study on parameters identification of one diode equivalent circuit of a solar cell module. Energy 93, 1515–1522 (2015)

    Article  Google Scholar 

  21. Marinakis, Y., Marinaki, M.: A bumble bees mating optimization algorithm for the open vehicle routing problem. Swarm Evol. Comput. 15, 80–94 (2014). Elsevier

    Article  MATH  Google Scholar 

  22. Xu, W., Wang, R., Yang, J.: An improved league championship algorithm with free search and its application on production scheduling. J. Intell. Manuf., 1–10 (2015). Springer. Journal no. 10845. https://doi.org/10.1007/s10845-015-1099-4

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Acknowledgements

I would like to thank to my students Ashley Raven [14] and Petar Tashkov [15] for the design, implementation and overclocking of computer systems used for the experiments presented in this article.

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Authors and Affiliations

  1. School of Media, Art and Technology, Southampton Solent University, East Park Terrace, Southampton, SO14 0YN, UK

    Kalin Penev

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  1. Kalin Penev
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Correspondence to Kalin Penev .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

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Penev, K. (2018). Free Search in Multidimensional Space M. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2017. Lecture Notes in Computer Science(), vol 10665. Springer, Cham. https://doi.org/10.1007/978-3-319-73441-5_43

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  • DOI: https://doi.org/10.1007/978-3-319-73441-5_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73440-8

  • Online ISBN: 978-3-319-73441-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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