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

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.

SUBMITTER: Rehman MZ

PROVIDER: S-EPMC8345889 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Rehman Muhammad Zubair MZ Khan Abdullah A Ghazali Rozaida R Aamir Muhammad M Nawi Nazri Mohd NM

PloS one 20210806 8

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/ ...[more]

PMID: 34358237

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