Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Multi-disciplinary Trends in Artificial Intelligence

MIWAI 2016: Multi-disciplinary Trends in Artificial Intelligence pp 76–86Cite as

Shape Optimization in Product Design Using Interactive Genetic Algorithm Integrated with Multi-objective Optimization

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10053))

Abstract

This paper proposes an interactive genetic algorithm (IGA) integrated with multi-objective genetic algorithm (MOGA) in development of a generative design system. IGA is used in initializing and handling single dimensionally qualitative objectives. MOGA is used in optimizing two quantitative objectives. Qualitative factors are considered as design objectives to be optimized together with quantitative criteria. The multi-objective optimization is regarded to concurrent handling of two quantitative criteria. Shape of product is modeled by parametric modeling with Rhinoceros and Grasshopper. IGA is processed using Galapagos in Grasshopper. Shape optimization of the product is processed by using MOGA in MATLAB and linked to Grasshopper. Pareto-optimal front is generated to show the optimal solutions, which is able to support designers in decision making. The perfume bottle design is used as an illustration of the proposed framework, but the framework is applicable to other design problems.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Sequin, C.H.: Virtual prototyping of scherk-collins saddle rings. Leonardo 30, 89–96 (1997)

    Article  Google Scholar 

  2. Sequin, C.H.: CAD tools for aesthetic engineering. Comput. Aided Des. 37, 737–750 (2005)

    Article  Google Scholar 

  3. Sequin, C.H.: Computer-aided design and realization of geometrical sculptures. Comput.-Aided Des. Appl. Spec. Issue CAD Arts 4, 671–681 (2007)

    Google Scholar 

  4. French, M.J.: Conceptual Design for Engineers. Springer, London (1999)

    Book  Google Scholar 

  5. Kolli, R., Pasman, G.J., Hennessey, J.M.: Some considerations for designing a user environment for creative ideation. In: Proceedings of INTERFACE 1993, Human Factors and Ergonomics Society, Santa Monica, USA, pp. 72–77 (1993)

    Google Scholar 

  6. Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89, 1275–1296 (2001)

    Article  Google Scholar 

  7. Brintrup, A.M., Ramsden, J., Tiwari, A.: An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization. Comput. Ind. 58, 279–291 (2007)

    Article  Google Scholar 

  8. Hu, Z.-H., Ding, Y.-S., Zhang, W.-B., Yan, Q.: An interactive co-evolutionary CAD system for garment pattern design. Comput. Aided Des. 40, 1094–1104 (2008)

    Article  Google Scholar 

  9. Sun, X., Gong, D., Zhang, W.: Interactive genetic algorithms with large population and semi-supervised learning. Appl. Soft Comput. 12, 3004–3013 (2012)

    Article  Google Scholar 

  10. Lu, S., Mok, P.Y., Jin, X.: From design methodology to evolutionary design: An interactive creation of marble-like textile patterns. Eng. Appl. Artif. Intell. 32, 124–135 (2014)

    Article  Google Scholar 

  11. Dou, R., Zong, C., Nan, G.: Multi-stage interactive genetic algorithm for collaborative product customization. Knowl.-Based Syst. 92, 43–54 (2016)

    Article  Google Scholar 

  12. Deb, K.: Multi-objective optimization using evolutionary algorithms: an introduction. KanGAL Report Number 2011003 (2011)

    Google Scholar 

  13. Shibuya, M., Kita, H., Kobayashi, S.: Integration of multi-objective and interactive genetic algorithms and its application to animation design. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 646–651. IEEE Press, New York (1999)

    Google Scholar 

  14. Brintrup, A.M., Ramsden, J., Takagi, H., Tiwari, A.: Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms. IEEE Trans. Evol. Comput. 12, 343–354 (2008)

    Article  Google Scholar 

  15. Zhang, L., Zhang, L., Wang, Y.: Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Sol. Energy 132, 38–50 (2016)

    Article  Google Scholar 

  16. Deb, K., Kalyanmoy, D.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  17. Grasshopper http://www.grasshopper3d.com/group/galapagos

  18. MathWorks https://www.mathworks.com/help/gads/gamultiobj.html

Download references

Acknowledgement

The research has been carried out as part of the research projects funded by National Research Council of Thailand and Naresaun University with Contract No. R2560B005. The author would like to gratefully thank all participants for their collaborations in this research.

Author information

Authors and Affiliations

  1. iD3 -Industrial Design, Decision and Development Research Unit, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand

    Somlak Wannarumon Kielarova

  2. Department of Industrial Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand

    Sunisa Sansri

Authors
  1. Somlak Wannarumon Kielarova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunisa Sansri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Somlak Wannarumon Kielarova .

Editor information

Editors and Affiliations

  1. Mahasarakham University , Maha Sarakham, Thailand

    Chattrakul Sombattheera

  2. Harz University of Applied Sciences , Wernigerode, Germany

    Frieder Stolzenburg

  3. University of Science and Technology , Hong Kong, China

    Fangzhen Lin

  4. Macquarie University , North Ryde, New South Wales, Australia

    Abhaya Nayak

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Kielarova, S.W., Sansri, S. (2016). Shape Optimization in Product Design Using Interactive Genetic Algorithm Integrated with Multi-objective Optimization. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49397-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49396-1

  • Online ISBN: 978-3-319-49397-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation