A Multiobjective Optimisation Approach for the Conceptual Design of Frame Structures

  • A. Suppapitnarm
  • G. T. Parks
  • K. Shea
  • P. J. Clarkson
Conference paper

Abstract

This paper explores the potential for using optimisation methods in the conceptual design of frame structures. The key elements of our approach are a randomised search based optimisation method (to simulate creativity), a generative structural shape grammar (to allow different configurations to be explored), and a multiobjective optimisation approach (to identify competing concepts occupying different parts of the trade-off surface). The results presented for a modified version of a classic structural optimisation problem demonstrate the success of this approach in exploring a multiplicity of different design configurations and presenting the designer with a variety of Pareto-optimal concepts worthy of further consideration.

Keywords

Multiobjective Optimisation Frame Structure Golden Ratio Grammar Rule Modification Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shea K, (2001) An Approach to Multiobjective Optimisation for Parametric Synthesis. Procs. 13th Int. Conf. Engineering Design, Glasgow, UK; 203–210.Google Scholar
  2. 2.
    Kirkpatrick S, Gerlatt CD Jr, Vecchi MP, (1983) Optimization by Simulated Annealing. Science, 220; 671–680.MATHGoogle Scholar
  3. 3.
    Cagan J, Mitchell WJ, (1993) Optimally Directed Shape Generation by Shape Annealing. Environment and Planning B-Planning & Design, 20(1); 5–12.CrossRefGoogle Scholar
  4. 4.
    Cagan J, Mitchell WJ, (1994) A Grammatical Approach to Network Flow Synthesis. IFIP Transactions B-Applications in Technology, 18; 173–189.Google Scholar
  5. 5.
    Reddy GM, Cagan J, (1995) Optimally Directed Truss Topology Generation Using Shape Annealing. Journal of Mechanical Design, 117(1); 206–209.CrossRefGoogle Scholar
  6. 6.
    Brown KN, Cagan J, (1997) Optimized Process Planning by Generative Simulated Annealing. AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 11(3); 219–235.CrossRefGoogle Scholar
  7. 7.
    Shea K, Cagan J, (1997) Innovative Dome Design: Applying Geodesic Patterns with Shape Annealing. AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 11(5); 379–394.CrossRefGoogle Scholar
  8. 8.
    Goldberg DE, (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley.Google Scholar
  9. 9.
    Schwefel H-P, (1995) Evolution and Optimum Seeking. Wiley Interscience, John Wiley & Sons.Google Scholar
  10. 10.
    Gips J, Stiny G, (1980) Production Systems and Grammars: A Uniform Characterization. Environment and Planning B-Planning & Design, 7(4); 399–408.CrossRefGoogle Scholar
  11. 11.
    Stiny G, (1980) Introduction to Shape and Shape Grammars. Environment and Planning B-Planning & Design, 7(3); 343–351.CrossRefGoogle Scholar
  12. 12.
    Shea K, Cagan J, (1999) The Design of Novel Roof Trusses with Shape Annealing: Assessing the Ability of a Computational Method in Aiding Structural Designers with Varying Design Intent. Design Studies, 20; 3–23.CrossRefGoogle Scholar
  13. 13.
    Suppapitnarm A, Shea K, Parks GT, Clarkson P J, (2000) Topological Optimisation of Bicycle Frames Using a Structural Shape Grammar. Procs. 2nd ASMO UK/ISSMO Conf. Engineering Design Optimization, Swansea, UK; 211–218.Google Scholar
  14. 14.
    Suppapitnarm A, Seffen KA, Parks GT, Clarkson P J, (2000) A Simulated Annealing Algorithm for Multiobjective Optimisation. Engineering Optimization, 33(10); 59–85.CrossRefGoogle Scholar
  15. 15.
    Zitzler E, Thiele L, (1999) Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4); 257–271.CrossRefGoogle Scholar
  16. 16.
    Corne DW, Knowles JD, Oates MJ, (2000) The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization, Procs. Parallel Problem Solving from Nature VI, Paris, France; 839–848.Google Scholar
  17. 17.
    Deb K, Agrawal S, Pratab A, Meyarivan T (2000) A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. Procs. Parallel Problem Solving from Nature VI, Paris, France; 849–858.Google Scholar
  18. 18.
    Knowles JD, Corne DW, (2000) Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8 (2); 149–172.CrossRefGoogle Scholar
  19. 19.
    Hansen MP, (1997) Tabu Search in Multiobjective Optimisation: MOTS. Procs. 13th Int. Conf. Multiple Criteria Decision Making, Cape Town, South Africa.Google Scholar
  20. 20.
    Bennage WA, Dhingra AK, (1995) Single and Multiobjective Structural Optimization in Discrete-continuous Variables Using Simulated Annealing. International Journal for Numerical Methods in Engineering, 38(16); 2753–2773.MATHCrossRefGoogle Scholar
  21. 21.
    Gobat JI, Atkinson DC, (1995) FElt: User’s Guide and Reference Manual. University of California, San Diego, USA.Google Scholar
  22. 22.
    Shea K, Cagan J, (1999) Languages and Semantics of Grammatical Discrete Structures. AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 13(4); 241–251.Google Scholar
  23. 23.
    Stiny G, Gips J, (1978) Algorithmic Aesthetics. University of California Press.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • A. Suppapitnarm
    • 1
  • G. T. Parks
    • 1
  • K. Shea
    • 1
  • P. J. Clarkson
    • 1
  1. 1.Engineering Design Centre Cambridge University Engineering DepartmentCambridgeUSA

Personalised recommendations