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HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET

The hybrid sine cosine and fitness dependent optimizer (SC-FDO) introduces four modifications to the original fitness dependent optimizer (FDO) algorithm to improve its exploit-explore tradeoff with a faster convergence speed. The modifications include a modified pace-updating equation, a random wei...

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Main Authors: Chiu, Po Chan, Ali, Selamat, Kuok, King Kuok
Format: Book Chapter
Language:English
Published: Cambridge Scholars Publishing 2024
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/46905/1/Hybrid%20Sine%20Cosine%20and%20Fitness.pdf
http://ir.unimas.my/id/eprint/46905/
https://www.cambridgescholars.com/product/978-1-0364-0804-6
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spelling my.unimas.ir-469052024-12-24T03:31:12Z http://ir.unimas.my/id/eprint/46905/ HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET Chiu, Po Chan Ali, Selamat Kuok, King Kuok T Technology (General) The hybrid sine cosine and fitness dependent optimizer (SC-FDO) introduces four modifications to the original fitness dependent optimizer (FDO) algorithm to improve its exploit-explore tradeoff with a faster convergence speed. The modifications include a modified pace-updating equation, a random weight factor and global fitness weight strategy, a conversion parameter strategy, and a best solution-updating strategy. This chapter evaluates the generalization ability of the hybrid SC-FDO-based neural network (SC-FDONN) in handling missing data imputation challenges that exhibit different percentages of missingness. The hybrid SC-FDONN's performance was evaluated using hold-out and cross-validation techniques. The findings revealed that the SC-FDONN outperformed all the benchmarks by an average accuracy of 94.3%. Therefore, the hybrid optimizer, SC-FDONN, is an effective technique for handling different percentages of missing data problems. Cambridge Scholars Publishing Kuok, King Kuok Rezaur, Rahman 2024-08-30 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/46905/1/Hybrid%20Sine%20Cosine%20and%20Fitness.pdf Chiu, Po Chan and Ali, Selamat and Kuok, King Kuok (2024) HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET. In: Metaheuristic Algorithms and Neural Networks in Hydrology. Cambridge Scholars Publishing, pp. 208-230. ISBN 978-1-0364-0804-6 https://www.cambridgescholars.com/product/978-1-0364-0804-6
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Chiu, Po Chan
Ali, Selamat
Kuok, King Kuok
HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
description The hybrid sine cosine and fitness dependent optimizer (SC-FDO) introduces four modifications to the original fitness dependent optimizer (FDO) algorithm to improve its exploit-explore tradeoff with a faster convergence speed. The modifications include a modified pace-updating equation, a random weight factor and global fitness weight strategy, a conversion parameter strategy, and a best solution-updating strategy. This chapter evaluates the generalization ability of the hybrid SC-FDO-based neural network (SC-FDONN) in handling missing data imputation challenges that exhibit different percentages of missingness. The hybrid SC-FDONN's performance was evaluated using hold-out and cross-validation techniques. The findings revealed that the SC-FDONN outperformed all the benchmarks by an average accuracy of 94.3%. Therefore, the hybrid optimizer, SC-FDONN, is an effective technique for handling different percentages of missing data problems.
author2 Kuok, King Kuok
author_facet Kuok, King Kuok
Chiu, Po Chan
Ali, Selamat
Kuok, King Kuok
format Book Chapter
author Chiu, Po Chan
Ali, Selamat
Kuok, King Kuok
author_sort Chiu, Po Chan
title HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
title_short HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
title_full HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
title_fullStr HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
title_full_unstemmed HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
title_sort hybrid sine cosine and fitness dependent optimizer for incomplete dataset
publisher Cambridge Scholars Publishing
publishDate 2024
url http://ir.unimas.my/id/eprint/46905/1/Hybrid%20Sine%20Cosine%20and%20Fitness.pdf
http://ir.unimas.my/id/eprint/46905/
https://www.cambridgescholars.com/product/978-1-0364-0804-6
_version_ 1819914971980496896
score 13.223943

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