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Adaptation and Hybridization in Computational Intelligence pp 111–128Cite as

Study of Lagrangian and Evolutionary Parameters in Krill Herd Algorithm

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Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 18))

Abstract

Krill Herd (KH) is a novel swarm-based intelligent optimization method developed through the idealization of the krill swarm. In the basic KH method, all the movement parameters used are originated from real nature-driven data found in the literature. The parameter setting based on such data is not necessarily the best selection. In this work, a systematic method is presented for the selection of the best parameter setting for the KH algorithm through an extensive study of arrays of high-dimensional benchmark problems. An important finding is that the best performance of KH can be obtained by setting effective coefficient of the krill individual (C best ), food coefficient(C food ), maximum diffusion speed (D max ), crossover probability (C r ) and mutation probability (M u ) parameters to 4.00, 4.25, 0.014, 0.225, and 0.025, respectively. This finding would eliminate the concerns regarding the optimal tuning of the KH algorithm for its most future applications.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China

    Gai-Ge Wang

  2. Department of Civil Engineering, The University of Akron, Akron, OH, 44325, USA

    Amir H. Gandomi

  3. Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, East Lansing, MI, 48824, USA

    Amir H. Alavi

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  1. Gai-Ge Wang
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  2. Amir H. Gandomi
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  3. Amir H. Alavi
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Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister Jr.

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Wang, GG., Gandomi, A.H., Alavi, A.H. (2015). Study of Lagrangian and Evolutionary Parameters in Krill Herd Algorithm. In: Fister, I., Fister Jr., I. (eds) Adaptation and Hybridization in Computational Intelligence. Adaptation, Learning, and Optimization, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-14400-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-14400-9_5

  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-14400-9

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