Sine–cosine crow search algorithm: theory and applications

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.

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Correspondence to Seyed Hamid Reza Pasandideh.

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Khalilpourazari, S., Pasandideh, S.H.R. Sine–cosine crow search algorithm: theory and applications. Neural Comput & Applic 32, 7725–7742 (2020). https://doi.org/10.1007/s00521-019-04530-0

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Keywords

  • Sine–cosine crow search algorithm
  • Global optimization
  • Crow search algorithm
  • Sine–cosine algorithm