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An introduction of Krill Herd algorithm for engineering optimization.

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Title: An introduction of Krill Herd algorithm for engineering optimization.
Authors: Gandomi, Amir H.1, Alavi, Amir H.2
Source: Journal of Civil Engineering & Management. Apr2016, Vol. 22 Issue 3, p302-310. 9p.
Subjects: Metaheuristic algorithms, Engineering databases, Computational aeroacoustics, Gandomi, A. H., Alavi, A. H.
Abstract: A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is compared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods. [ABSTRACT FROM PUBLISHER]
Copyright of Journal of Civil Engineering & Management is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: An introduction of Krill Herd algorithm for engineering optimization.
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  Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+databases%22">Engineering databases</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+aeroacoustics%22">Computational aeroacoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Gandomi%2C+A%2E+H%2E%22">Gandomi, A. H.</searchLink><br /><searchLink fieldCode="DE" term="%22Alavi%2C+A%2E+H%2E%22">Alavi, A. H.</searchLink>
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  Data: A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is compared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of Journal of Civil Engineering & Management is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3846/13923730.2014.897986
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        Text: English
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      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Engineering databases
        Type: general
      – SubjectFull: Computational aeroacoustics
        Type: general
      – SubjectFull: Gandomi, A. H.
        Type: general
      – SubjectFull: Alavi, A. H.
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              Text: Apr2016
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