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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2020

18.10.2019 | Soft Computing Techniques: Applications and Challenges

Sine–cosine crow search algorithm: theory and applications

verfasst von: Soheyl Khalilpourazari, Seyed Hamid Reza Pasandideh

Erschienen in: Neural Computing and Applications | Ausgabe 12/2020

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Abstract

In this paper, we propose a new hybrid algorithm called sine–cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine–cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms.
Vorheriger Artikel A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks
Nächster Artikel Editorial to special issue on hybrid artificial intelligence and machine learning technologies in intelligent systems

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Metadaten
Titel
Sine–cosine crow search algorithm: theory and applications
verfasst von
Soheyl Khalilpourazari
Seyed Hamid Reza Pasandideh
Publikationsdatum
18.10.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04530-0

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