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Engineering Optimization and Industrial Applications

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Surrogate-Based Modeling and Optimization

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Abstract

Design optimization is important in engineering and industrial applications. It is usually very challenging to find optimum designs, which require both efficient optimization algorithms and high-quality simulators that are often time-consuming. To some extent, an optimization process is equivalent to a self-organizing system, and the organized states are the optima that are to be searched for. In this chapter, we discuss both optimization and self-organization in a unified framework, and we use three metaheuristic algorithms, the firefly algorithm, the bat algorithm and cuckoo search, as examples to see how this self-organized process works. We then present a set of nine design problems in engineering and industry. We also discuss the challenging issues that need to be addressed in the near future.

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Notes

  1. 1.

    http://www.mathworks.com/matlabcentral/fileexchange/29693-firefly-algorithm.

  2. 2.

    www.mathworks.com/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm.

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Authors and Affiliations

  1. School of Science and Technology, Middlesex University, London, NW4 4BT, UK

    Xin-She Yang

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  1. Xin-She Yang
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Correspondence to Xin-She Yang .

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  1. , Engineering Optimization & Modeling Cent, Reykjavik University, Menntavegur 1, Reykjavik, 101, Iceland

    Slawomir Koziel

  2. School of Science and Engineering, Engineering Optimization & Modeling, Reykjavik University, Menntavegur 1, Reykjavik, 101, Iceland

    Leifur Leifsson

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Yang, XS. (2013). Engineering Optimization and Industrial Applications. In: Koziel, S., Leifsson, L. (eds) Surrogate-Based Modeling and Optimization. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7551-4_16

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