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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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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DOI: https://doi.org/10.1007/978-1-4614-7551-4_16
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