Skip to main content

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2))

Included in the following conference series:

  • 1404 Accesses

Abstract

A new simulation optimization algorithm, named ITO Algorithm, is proposed in this paper. The ITO algorithm is inspired by the Brown motion of particles in liquid. And many famous scientists have studied the particles motion and proposed independence increment theory. The ITO algorithm is based on the Ito stochastic process. And the experiments show its convergence speed is fast. In this paper the ITO algorithm is applied in the Stochastic Inventory System, and the experiment results show that the ITO algorithm can find the best solutions for the Inventory models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Sanchez, P.J.: As Simple as Possible, But No Simpler: A Gentle Introduction to Simulation Modeling. In: Proceedings of The 2006 Winter Simulation Conference. U.S.A.: Monterey, CA93943, pp. 1–10 (2006)

    Google Scholar 

  2. Charles, M.M., Michael J.N.: Tutorial on Agent-Based Modeling and Simulation Part 2: How to Model with Agents. In: Proceedings of The 2005 Winter Simulation Conference. Argonne, U.S.A.: Argonne National Laboratory, IL 60439, pp. 73–83 (2005)

    Google Scholar 

  3. Stephen, E.C.: Bayesian Ideas and Discrete Event Simulation: Why, What and How. In: Proceedings of The 2004 Winter Simulation Conference. Fontainebleau, FRANCE: 77300, pp. 96–106 (2004)

    Google Scholar 

  4. Kleijnen, J.P.C.: White Noise Assumptions Revisited: Regression Meta-Models and Experimental Designs in Practice. In: Proceedings of The 2003 Winter Simulation Conference. LE 5000, Tilburg, The Netherlands, pp. 107–117 (2003)

    Google Scholar 

  5. Olafsson, S., Kim, J.: Simulation Optimization. In: Proceedings of The 2002 Winter Simulation Conference. 2019 Black Engineering, Ames, U.S.A.: IA50011, pp. 79–84 (2002)

    Google Scholar 

  6. Kim, S.: Gradient-Based Simulation Optimization. In: Proceedings of The 2001 Winter Simulation Conference. WestLafayette, U.S.A.: IN47907, pp. 159–167 (2001)

    Google Scholar 

  7. Pan, Z.J., Kang, L.S., Chen, Y.P.: Evolutionary Computation. Tsinghua University Press, Beijing (1998)

    Google Scholar 

  8. Yao, X., Xu, Y.: Recent Advances in Evolutionary Computation. Computer Science & Technology 21(1), 1–18 (2006)

    Article  Google Scholar 

  9. Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proc. congress on Evolutionary Computation 2001 IEEE Service Center, Piscataway, NJ., Seoul, Korea, pp. 157–169 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

De-Shuang Huang Laurent Heutte Marco Loog

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dong, W., Zhang, D., Weicheng, Z., Leng, J. (2007). The Simulation Optimization Algorithm Based on the Ito Process. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74282-1_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74281-4

  • Online ISBN: 978-3-540-74282-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics