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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| Published in | PloS one Vol. 16; no. 8; p. e0255269 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
06.08.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0255269 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Rehman, Muhammad Zubair Ghazali, Rozaida Khan, Abdullah Nawi, Nazri Mohd Aamir, Muhammad |
| AuthorAffiliation | 2 Institute of Computer Sciences and IT (ICS/IT), The University of Agriculture, Peshawar, Pakistan 1 Soft Computing & Data Mining Centre (SMC), Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia Torrens University Australia, AUSTRALIA |
| AuthorAffiliation_xml | – name: 1 Soft Computing & Data Mining Centre (SMC), Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia – name: 2 Institute of Computer Sciences and IT (ICS/IT), The University of Agriculture, Peshawar, Pakistan – name: Torrens University Australia, AUSTRALIA |
| Author_xml | – sequence: 1 givenname: Muhammad Zubair orcidid: 0000-0003-2589-4189 surname: Rehman fullname: Rehman, Muhammad Zubair – sequence: 2 givenname: Abdullah surname: Khan fullname: Khan, Abdullah – sequence: 3 givenname: Rozaida orcidid: 0000-0003-0119-0873 surname: Ghazali fullname: Ghazali, Rozaida – sequence: 4 givenname: Muhammad orcidid: 0000-0002-4999-7740 surname: Aamir fullname: Aamir, Muhammad – sequence: 5 givenname: Nazri Mohd orcidid: 0000-0003-0685-7892 surname: Nawi fullname: Nawi, Nazri Mohd |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34358237$$D View this record in MEDLINE/PubMed |
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| Snippet | The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search... |
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| SubjectTerms | Algorithms Analysis Biology and Life Sciences Computer and Information Sciences Ecology and Environmental Sciences Learning strategies Mathematical optimization Methods Physical Sciences Research and Analysis Methods |
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| Title | A new Multi Sine-Cosine algorithm for unconstrained optimization problems |
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