Skip to main content
SpringerLink
Log in

Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems

  • Conference paper
Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

Included in the following conference series:

Abstract

We investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems. For this purpose, a penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated on four well–known engineering problems with promising results. Comparisons with the standard local and global variant of Particle Swarm Optimization are reported and discussed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
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.

References

  1. Floudas, C.A., Pardalos, P.M.: A collection of test problems for constrained global optimization algorithms. In: Floudas, C.A., Pardalos, P.M. (eds.) A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)

    Google Scholar 

  2. Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)

    MATH  Google Scholar 

  3. Coello Coello, C.A.: A survey of constraint handling techniques used with evolutionary algorithms. Techn. Rep. Lania–RI–99–04, LANIA (1999)

    Google Scholar 

  4. Hu, X., Eberhart, R.C., Shi, Y.: Engineering optimization with particle swarm. In: Proc. 2003 IEEE Swarm Intelligence Symposium, pp. 53–57 (2003)

    Google Scholar 

  5. Joines, J.A., Houck, C.R.: On the use of non–stationary penalty functions to solve nonlinear constrained optimization problems with ga’s. In: Proc. IEEE Int. Conf. Evol. Comp., pp. 579–585 (1994)

    Google Scholar 

  6. Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. In: Sincak, et al. (eds.) Intelligent Technologies–Theory and Application: New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 214–220. IOS Press, Amsterdam (2002)

    Google Scholar 

  7. Yeniay, Ö.: Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications 10, 45–56 (2005)

    Google Scholar 

  8. Coello Coello, C.A.: Self–adaptive penalties for ga–based optimization. In: Proc. 1999 IEEE CEC, Washington, D.C., USA, vol. 1, pp. 573–580 (1999)

    Google Scholar 

  9. Coello Coello, C.A.: Use of a self–adaptive penalty approach for engineering optimization problems. Computers in Industry 41, 113–127 (2000)

    Article  Google Scholar 

  10. Parsopoulos, K.E., Vrahatis, M.N.: UPSO: A unified particle swarm optimization scheme. In: Proc. Int. Conf. Computational Methods in Sciences and Engineering (ICCMSE 2004). Lecture Series on Computer and Computational Sciences, vol. 1, pp. 868–873. VSP International Science Publishers, Zeist (2004)

    Google Scholar 

  11. Parsopoulos, K.E., Vrahatis, M.N.: Unified particle swarm optimization in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 590–599. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings Sixth Symposium on Micro Machine and Human Science, Piscataway, NJ, pp. 39–43. IEEE Service Center, Los Alamitos (1995)

    Chapter  Google Scholar 

  13. Clerc, M., Kennedy, J.: The particle swarm–explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)

    Article  Google Scholar 

  14. Arora, J.S.: Introduction to Optimum Design. McGraw-Hill, New York (1989)

    Google Scholar 

  15. Rao, S.S.: Engineering Optimization–Theory and Practice. Wiley, Chichester (1996)

    Google Scholar 

  16. Sandgen, E.: Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design (ASME) 112, 223–229 (1990)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras, GR–26110, Patras, Greece

    K. E. Parsopoulos

  2. University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR–26110, Patras, Greece

    M. N. Vrahatis

Authors
  1. K. E. Parsopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. N. Vrahatis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Parsopoulos, K.E., Vrahatis, M.N. (2005). Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_71

Download citation

  • DOI: https://doi.org/10.1007/11539902_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation