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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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
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