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Soft Computing
Erschienen in: Soft Computing 8/2017

30.10.2015 | Methodologies and Application

Global harmony search with generalized opposition-based learning

verfasst von: Zhaolu Guo, Shenwen Wang, Xuezhi Yue, Huogen Yang

Erschienen in: Soft Computing | Ausgabe 8/2017

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Abstract

Harmony search (HS) has shown promising performance in a wide range of real-world applications. However, in many cases, the basic HS exhibits strong exploration ability but weak exploitation capability. In order to enhance the exploitation capability of the basic HS, this paper presents an improved global harmony search with generalized opposition-based learning strategy (GOGHS). In GOGHS, the valuable information from the best harmony is utilized to enhance the exploitation capability. Moreover, the generalized opposition-based learning (GOBL) strategy is incorporated to increase the probability of finding the global optimum. The performance of GOGHS is evaluated on a set of benchmark test functions and is compared with several HS variants. The experimental results show that GOGHS can obtain competitive results.
Vorheriger Artikel Differential Evolution with Grid-Based Parameter Adaptation
Nächster Artikel GA-SVM modeling of multiclass seizure detector in epilepsy analysis system using cloud computing

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Metadaten
Titel
Global harmony search with generalized opposition-based learning
verfasst von
Zhaolu Guo
Shenwen Wang
Xuezhi Yue
Huogen Yang
Publikationsdatum
30.10.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 8/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1912-1

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