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Stochastic Optimisation in Computational Engineering Design

  • Conference paper
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 175))

Abstract

It is evident that the requirements and specifications for engineering products, as well as the demand for these products, have increased substantially over the last couple of decades. As a result, various engineering design tasks have become considerably more complex. These observations and facts have necessitated the development of new design approaches that offer alternatives to the traditional ways of exploring design spaces and performing engineering design. At the same time, the Mathematical Sciences have produced a number of advanced search and optimisation algorithms that can explore and assess challenging and complicated models and functions. Furthermore, significant advances have been made in the field of Information Technology in terms of utilising massively parallel computational power. It has been shown that all these advancements can be exploited in complementary and synergistic ways when combined appropriately, producing a complete computational engineering design system. In this paper, a guide for deploying all these available technologies in efficient and appropriate ways is presented, illustrated with applications to real-world engineering problems in which not only are innovative solutions produced but also previously unidentified avenues of research are revealed.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK

    Timoleon Kipouros

  2. School of Engineering, Department of Power and Propulsion, Cranfield University, Cranfield, MK43 0AL, UK

    Timoleon Kipouros

Authors
  1. Timoleon Kipouros
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Correspondence to Timoleon Kipouros .

Editor information

Editors and Affiliations

  1. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Oliver Schütze

  2. , Computer Science Department, CINVESTAV-IPN, Col. San Pedro Zacatenco 2508, Mexico City, 07360, Mexico

    Carlos A. Coello Coello

  3. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  4. Computer Science and Communications, Research Unit, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Emilia Tantar

  5. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

    Pascal Bouvry

  6. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

  7. Bâtiment Leyteire, UFR Sciences et Modélisation, Université Bordeaux II, 3ter place de la Victoire, Bordeaux, 33000, France

    Pierrick Legrand

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Kipouros, T. (2013). Stochastic Optimisation in Computational Engineering Design. In: Schütze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31519-0_31

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  • DOI: https://doi.org/10.1007/978-3-642-31519-0_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31518-3

  • Online ISBN: 978-3-642-31519-0

  • eBook Packages: EngineeringEngineering (R0)

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