Skip to main content
SpringerLink
Log in

Some Guidelines for Genetic Algorithm Implementation in MINLP Batch Plant Design Problems

  • Chapter
Advances in Metaheuristics for Hard Optimization

Part of the book series: Natural Computing Series ((NCS))

Abstract

In recent decades, a novel class of optimization techniques, namely metaheuristics, has been developed and devoted to the solution of highly combinatorial discrete problems.The improvements provided by these methods were extended to the continuous or mixed-integer optimization area. This chapter addresses the problem of adapting a Genetic Algorithm (GA) to a Mixed Integer Non-linear Programming (MINLP) problem.The basis of the work is optimal batch plant design, which is of great interest in the framework of Process Engineering. This study deals with the two main issues for GAs, i.e. the treatment of continuous variables by specific encoding and efficient constraints handling in GA. Various techniques are tested for both topics and numerical results show that the use of a mixed real-discrete encoding and a specific domination-based tournament method is the most appropriate approach.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Teh YS, Rangaiah GP (2003) Comput Chem Eng 27:1665–1679

    Google Scholar 

  2. Dedieu S, Pibouleau L, Azzaro-Pantel C, Domenech S (2003) Comput Chem Eng 27:1723–1740

    Google Scholar 

  3. Lee S, Grossmann IE (2000) Comput Chem Eng 24:2125–2141

    Google Scholar 

  4. Modi AK, Karimi IA (1989) Comput Chem Eng 13:127–139

    Google Scholar 

  5. Grossmann IE (2002) Opt Eng 3:227–252

    Google Scholar 

  6. Dietz A, Azzaro-Pantel C, Pibouleau L, Domenech S (2005) Ind Eng Chem Res 44:2191–2206

    Google Scholar 

  7. Hao JK, Galinier P, Habib M (1999) Rev Intell Art 1:11-48

    Google Scholar 

  8. Triki E, Collette Y, Siarry P (2005) Eur J Op Res 166:77–92

    Google Scholar 

  9. Hedar AR, Fukushia M (2006) Eur J Op Res 170:329–349

    Google Scholar 

  10. Ducek G, Scheuer T (1990) J Computat Phys 90:161–175

    Google Scholar 

  11. Ahmadi S, Osman IH (2005) Eur J Op Res 162:30–44

    Google Scholar 

  12. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1982) Optimization by Simulated Annealing. IBM Research Report RC 9355

    Google Scholar 

  13. André J, Siarry P, Dognon P (2001) Adv Eng Softw 32:49–60

    Google Scholar 

  14. Beyer HG, Schwefel HP (2002) Nat Comput Int J 1:3–52

    Google Scholar 

  15. Yang YW, Xu JF, Soh CK (2006) Eur J Op Res 168:354–369

    Google Scholar 

  16. Grossmann IE, Sargent RWH (1979) Ind Eng Chem Proc Des Dev 18:343–348

    Google Scholar 

  17. Kocis GR, Grossmann IE (1988) Ind Eng Chem Res 27:1407–1421

    Google Scholar 

  18. Patel AN, Mah RSH, Karimi IA (1991) Comput Chem Eng 15:451–469

    Google Scholar 

  19. Wang C, Quan H, Xu X (1996) Ind Eng Chem Res 35:3560–3566

    Google Scholar 

  20. Wang C, Quan H, Xu X (1999) Comput Chem Eng 23:427–437

    Google Scholar 

  21. Wang C, Xin Z (2002) Comput Chem Eng 41:6678–6686

    Google Scholar 

  22. Ponsich A, Azzaro-Pantel C, Domenech S, Pibouleau L (2005) About the relevance of Mathematical Programming and stochastic optimisation methods : application to optimal batch plant design problems. In: Espuña and Puigjaner (eds) Proceedings of European Symposium of Computer Aided Process Engineering 15, Barcelona, May 30–June 1, 2005. Elsevier, New York

    Google Scholar 

  23. Brooke A, Kendrick D, Meeraus A, Raman R (1998) GAMS User’s Guide. GAMS Development Corporation, Washington DC

    Google Scholar 

  24. Pinto JM, Montagna JM, Vecchietti AR, Irribarren OA, Asenjo JA (2001) Biotechnol Bioeng 74:451–465

    Google Scholar 

  25. Epperly TGW, Ierapetritou MG, Pistikopoulos EN (1997) Comput Chem Eng 21:1411–1431

    Google Scholar 

  26. Aguilar Lasserre A, Azzaro-Pantel C, Domenech S, Pibouleau L (2005) Modélisation des imprécisions de la demande en conception optimale multicritère d’ateliers discontinus. Proceedings of SFGP Congress in Toulouse (France), September 20–22, 2005

    Google Scholar 

  27. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  28. Coello Coello CA (2002) Comput Meth Appl Mech Eng 191:1245–1287

    Google Scholar 

  29. Michalewicz Z, Dasgupta D, Le Riche RG, Schoenauer M (1996) Comput Ind Eng 30:851–870

    Google Scholar 

  30. Coello Coello CA (2002b) Comput Ind 41:113–127

    Google Scholar 

  31. Deb K (2000) Comput Meth Appl Mech Eng 186:311–338

    Google Scholar 

  32. Coello Coello CA, Mezura Montes E (2002) Adv Eng Inf 16:193–203

    Google Scholar 

  33. Golberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., MA

    Google Scholar 

  34. Montastruc L (2003) Développement d’un pilote automatisé, fiable et sûr pour la dépollution d’effluents aqueux d’origine industrielle. PhD Thesis, INP Toulouse, France

    Google Scholar 

  35. Michalewicz Z, Schoenauer M (1996) Evol Comp 4:1–32

    Google Scholar 

  36. Deb K, Agrawal RB (1995) Complex Sys 9:115–148

    Google Scholar 

  37. Raghuwanshi MM, Kakde OG (2005) Survey on multiobjective evolutionary and real coded genetic algorithms. Proceedings of the 7th International Conference on Adaptative and Natural Computing Algorithms, Coimbra (Portugal), March 21–23, 2005

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire de Génie Chimique de Toulouse, 31106, Toulouse, France

    Antonin Ponsich, Catherine Azzaro-Pantel, Serge Domenech & Luc Pibouleau

Authors
  1. Antonin Ponsich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Catherine Azzaro-Pantel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Serge Domenech
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luc Pibouleau
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Laboratory LiSSi, University of Paris 12, 61 Avenue du Général de Gaulle, 94010, Créteil, France

    Patrick Siarry

  2. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ponsich, A., Azzaro-Pantel, C., Domenech, S., Pibouleau, L. (2007). Some Guidelines for Genetic Algorithm Implementation in MINLP Batch Plant Design Problems. In: Siarry, P., Michalewicz, Z. (eds) Advances in Metaheuristics for Hard Optimization. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72960-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72960-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72959-4

  • Online ISBN: 978-3-540-72960-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation