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Metaheuristic Optimization: Nature-Inspired Algorithms and Applications

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Artificial Intelligence, Evolutionary Computing and Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 427))

Abstract

Turing’s pioneer work in heuristic search has inspired many generations of research in heuristic algorithms. In the last two decades, metaheuristic algorithms have attracted strong attention in scientific communities with significant developments, especially in areas concerning swarm intelligence based algorithms. In this work, we will briefly review some of the important achievements in metaheuristics, and we will also discuss key implications in applications and topics for further research.

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Authors and Affiliations

  1. Mathematics & Scientific Computing, National Physical Laboratory, Teddington, TW11 0LW, UK

    Xin-She Yang

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  1. Xin-She Yang
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  1. , Mathematics and Scientific Computing, National Physical Laboratory, Hampton Road, Teddington, TW11 0LW, United Kingdom

    Xin-She Yang

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Yang, XS. (2013). Metaheuristic Optimization: Nature-Inspired Algorithms and Applications. In: Yang, XS. (eds) Artificial Intelligence, Evolutionary Computing and Metaheuristics. Studies in Computational Intelligence, vol 427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29694-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-29694-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29693-2

  • Online ISBN: 978-3-642-29694-9

  • eBook Packages: EngineeringEngineering (R0)

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