Swarm and stochastic computing for global optimization

Yang, Xin-She ORCID logoORCID: https://orcid.org/0000-0001-8231-5556 (2021) Swarm and stochastic computing for global optimization. In: Handbook of Unconventional Computing - Volume 1: Theory. Adamatzky, Andrew, ed. WSPC Book Series in Unconventional Computing . World Scientific, pp. 469-487. ISBN 9789811235719, e-ISBN 9789811235726. [Book Section] (doi:10.1142/9789811235726_0015)

Abstract

Many problems in data mining and machine learning are related to optimization, and optimization techniques are often used to solve such problems. Traditional techniques such as gradient-based methods can be efficient, but they are local optimizers. For global optimization, alternative approaches tend to be nature-inspired metaheuristic algorithms. We introduce some of the nature-inspired optimization algorithms with the emphasis on their main characteristics. We also highlight the role of algorithmic components in such algorithms, and then we conclude with a brief discussion about some open problems.

Item Type: Book Section
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 33770
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Depositing User: Jisc Publications Router
Date Deposited: 02 Sep 2021 10:33
Last Modified: 02 Sep 2021 10:33
URI: https://eprints.mdx.ac.uk/id/eprint/33770

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