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A new Multi Sine-Cosine algorithm for unconstrained optimization problems

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  • Muhammad Zubair Rehman
  • Abdullah Khan
  • Rozaida Ghazali
  • Muhammad Aamir
  • Nazri Mohd Nawi

Abstract

The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.

Suggested Citation

  • Muhammad Zubair Rehman & Abdullah Khan & Rozaida Ghazali & Muhammad Aamir & Nazri Mohd Nawi, 2021. "A new Multi Sine-Cosine algorithm for unconstrained optimization problems," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0255269
    DOI: 10.1371/journal.pone.0255269
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