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An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator

The existing cuckoo search (CS) algorithm has the drawbacks of slow convergence speed, low convergence accuracy, and easy to fall into local optimum. An improved cuckoo search algorithm is proposed in this manuscript to overcome the mentioned shortages using elite opposition-based learning and golde...

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Main Authors: Li, Peng Cheng, Zhang, Xuan Yu, Mohd. Zain, Azlan, Zhou, Kai Qing
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/100468/
http://dx.doi.org/10.1007/978-3-031-06794-5_23
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spelling my.utm.1004682023-04-14T02:00:19Z http://eprints.utm.my/id/eprint/100468/ An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator Li, Peng Cheng Zhang, Xuan Yu Mohd. Zain, Azlan Zhou, Kai Qing QA75 Electronic computers. Computer science The existing cuckoo search (CS) algorithm has the drawbacks of slow convergence speed, low convergence accuracy, and easy to fall into local optimum. An improved cuckoo search algorithm is proposed in this manuscript to overcome the mentioned shortages using elite opposition-based learning and golden sine operator (EOBL-GS-CS). The modifications could be summarized from two aspects. On the one hand, the elite opposition-based learning (EOBL) mechanism is employed to improve the diversity and quality of the population, preventing the algorithm from falling into the local optimum. On the other hand, the golden sine operator accelerates the algorithm’s convergence speed and improves the algorithm's optimization ability. In the verification part, 14 unimodal and multimodal benchmark functions are used to highlight the characteristics of the proposed algorithm. The experimental results show that, compared with the standard CS and other variants, the EOBL-GS-CS has a faster convergence speed, higher solution accuracy, and significantly improved optimization performance. Springer Science and Business Media Deutschland GmbH 2022 Article PeerReviewed Li, Peng Cheng and Zhang, Xuan Yu and Mohd. Zain, Azlan and Zhou, Kai Qing (2022) An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13338 (NA). pp. 276-288. ISSN 0302-9743 http://dx.doi.org/10.1007/978-3-031-06794-5_23 DOI : 10.1007/978-3-031-06794-5_23
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Li, Peng Cheng
Zhang, Xuan Yu
Mohd. Zain, Azlan
Zhou, Kai Qing
An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
description The existing cuckoo search (CS) algorithm has the drawbacks of slow convergence speed, low convergence accuracy, and easy to fall into local optimum. An improved cuckoo search algorithm is proposed in this manuscript to overcome the mentioned shortages using elite opposition-based learning and golden sine operator (EOBL-GS-CS). The modifications could be summarized from two aspects. On the one hand, the elite opposition-based learning (EOBL) mechanism is employed to improve the diversity and quality of the population, preventing the algorithm from falling into the local optimum. On the other hand, the golden sine operator accelerates the algorithm’s convergence speed and improves the algorithm's optimization ability. In the verification part, 14 unimodal and multimodal benchmark functions are used to highlight the characteristics of the proposed algorithm. The experimental results show that, compared with the standard CS and other variants, the EOBL-GS-CS has a faster convergence speed, higher solution accuracy, and significantly improved optimization performance.
format Article
author Li, Peng Cheng
Zhang, Xuan Yu
Mohd. Zain, Azlan
Zhou, Kai Qing
author_facet Li, Peng Cheng
Zhang, Xuan Yu
Mohd. Zain, Azlan
Zhou, Kai Qing
author_sort Li, Peng Cheng
title An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
title_short An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
title_full An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
title_fullStr An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
title_full_unstemmed An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
title_sort improved cuckoo search algorithm using elite opposition-based learning and golden sine operator
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://eprints.utm.my/id/eprint/100468/
http://dx.doi.org/10.1007/978-3-031-06794-5_23
_version_ 1764222571233935360
score 13.211869

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