A new Multi Sine-Cosine algorithm for unconstrained optimization problems.

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...

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Autores principales:	Muhammad Zubair Rehman, Abdullah Khan, Rozaida Ghazali, Muhammad Aamir, Nazri Mohd Nawi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5b1b282389704e198dff4fa4844a913f
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id	oai:doaj.org-article:5b1b282389704e198dff4fa4844a913f
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spelling	oai:doaj.org-article:5b1b282389704e198dff4fa4844a913f2021-12-02T20:18:35ZA new Multi Sine-Cosine algorithm for unconstrained optimization problems.1932-620310.1371/journal.pone.0255269https://doaj.org/article/5b1b282389704e198dff4fa4844a913f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255269https://doaj.org/toc/1932-6203The 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.Muhammad Zubair RehmanAbdullah KhanRozaida GhazaliMuhammad AamirNazri Mohd NawiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255269 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad Zubair Rehman Abdullah Khan Rozaida Ghazali Muhammad Aamir Nazri Mohd Nawi A new Multi Sine-Cosine algorithm for unconstrained optimization problems.
description	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.
format	article
author	Muhammad Zubair Rehman Abdullah Khan Rozaida Ghazali Muhammad Aamir Nazri Mohd Nawi
author_facet	Muhammad Zubair Rehman Abdullah Khan Rozaida Ghazali Muhammad Aamir Nazri Mohd Nawi
author_sort	Muhammad Zubair Rehman
title	A new Multi Sine-Cosine algorithm for unconstrained optimization problems.
title_short	A new Multi Sine-Cosine algorithm for unconstrained optimization problems.
title_full	A new Multi Sine-Cosine algorithm for unconstrained optimization problems.
title_fullStr	A new Multi Sine-Cosine algorithm for unconstrained optimization problems.
title_full_unstemmed	A new Multi Sine-Cosine algorithm for unconstrained optimization problems.
title_sort	new multi sine-cosine algorithm for unconstrained optimization problems.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5b1b282389704e198dff4fa4844a913f
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A new Multi Sine-Cosine algorithm for unconstrained optimization problems.

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