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Optimization Algorithms

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Computational Optimization, Methods and Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 356))

Abstract

The right choice of an optimization algorithm can be crucially important in finding the right solutions for a given optimization problem. There exist a diverse range of algorithms for optimization, including gradient-based algorithms, derivative-free algorithms and metaheuristics. Modern metaheuristic algorithms are often nature-inspired, and they are suitable for global optimization. In this chapter, we will briefly introduce optimization algorithms such as hill-climbing, trust-region method, simulated annealing, differential evolution, particle swarm optimization, harmony search, firefly algorithm and cuckoo search.

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Author information

Authors and Affiliations

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

    Xin-She Yang

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  1. Xin-She Yang
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Editors and Affiliations

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

    Slawomir Koziel

  2. Mathematics and Scientific Computing, National Physical Laboratory, TW11 0LW, Teddington, UK

    Xin-She Yang

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© 2011 Springer-Verlag Berlin Heidelberg

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Yang, XS. (2011). Optimization Algorithms. In: Koziel, S., Yang, XS. (eds) Computational Optimization, Methods and Algorithms. Studies in Computational Intelligence, vol 356. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20859-1_2

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  • DOI: https://doi.org/10.1007/978-3-642-20859-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20858-4

  • Online ISBN: 978-3-642-20859-1

  • eBook Packages: EngineeringEngineering (R0)

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