A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection
Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct...
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ftdoajarticles:oai:doaj.org/article:78638bc8b8594daf88a54c26ff9c4a89 2023-05-15T15:51:10+02:00 A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection Sankalap Arora Harpreet Singh Manik Sharma Sanjeev Sharma Priyanka Anand 2019-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2019.2897325 https://doaj.org/article/78638bc8b8594daf88a54c26ff9c4a89 EN eng IEEE https://ieeexplore.ieee.org/document/8643348/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2019.2897325 https://doaj.org/article/78638bc8b8594daf88a54c26ff9c4a89 IEEE Access, Vol 7, Pp 26343-26361 (2019) Grey wolf optimizer crow search algorithm hybrid algorithm function optimization feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2019 ftdoajarticles https://doi.org/10.1109/ACCESS.2019.2897325 2022-12-31T13:41:58Z Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the state-of-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. Article in Journal/Newspaper Canis lupus Directory of Open Access Journals: DOAJ Articles IEEE Access 7 26343 26361 |
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Directory of Open Access Journals: DOAJ Articles |
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topic |
Grey wolf optimizer crow search algorithm hybrid algorithm function optimization feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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Grey wolf optimizer crow search algorithm hybrid algorithm function optimization feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971 Sankalap Arora Harpreet Singh Manik Sharma Sanjeev Sharma Priyanka Anand A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection |
topic_facet |
Grey wolf optimizer crow search algorithm hybrid algorithm function optimization feature selection Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
description |
Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the state-of-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. |
format |
Article in Journal/Newspaper |
author |
Sankalap Arora Harpreet Singh Manik Sharma Sanjeev Sharma Priyanka Anand |
author_facet |
Sankalap Arora Harpreet Singh Manik Sharma Sanjeev Sharma Priyanka Anand |
author_sort |
Sankalap Arora |
title |
A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection |
title_short |
A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection |
title_full |
A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection |
title_fullStr |
A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection |
title_full_unstemmed |
A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection |
title_sort |
new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection |
publisher |
IEEE |
publishDate |
2019 |
url |
https://doi.org/10.1109/ACCESS.2019.2897325 https://doaj.org/article/78638bc8b8594daf88a54c26ff9c4a89 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
IEEE Access, Vol 7, Pp 26343-26361 (2019) |
op_relation |
https://ieeexplore.ieee.org/document/8643348/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2019.2897325 https://doaj.org/article/78638bc8b8594daf88a54c26ff9c4a89 |
op_doi |
https://doi.org/10.1109/ACCESS.2019.2897325 |
container_title |
IEEE Access |
container_volume |
7 |
container_start_page |
26343 |
op_container_end_page |
26361 |
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1766386233909444608 |