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Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter

Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional lea...

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Main Authors:	Muhammad, Badaruddin, Ibrahim, Zuwairie, Shapiai, Mohd. Ibrahim, Mohamad, Mohd. Saberi, Mohd. Azmi, Kamil Zakwan, Mat Jusof, Mohd. Falfazli
Format:	Conference or Workshop Item
Published:	2019
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/97106/ http://dx.doi.org/10.1109/ICCISci.2019.8716382
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id	my.utm.97106
record_format	eprints
spelling	my.utm.971062022-09-23T01:22:19Z http://eprints.utm.my/id/eprint/97106/ Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter Muhammad, Badaruddin Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Mohamad, Mohd. Saberi Mohd. Azmi, Kamil Zakwan Mat Jusof, Mohd. Falfazli T Technology (General) Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning with a jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases. 2019 Conference or Workshop Item PeerReviewed Muhammad, Badaruddin and Ibrahim, Zuwairie and Shapiai, Mohd. Ibrahim and Mohamad, Mohd. Saberi and Mohd. Azmi, Kamil Zakwan and Mat Jusof, Mohd. Falfazli (2019) Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter. In: 2019 International Conference on Computer and Information Sciences, ICCIS 2019, 3 - 4 April 2019, Sakaka, Saudi Arabia. http://dx.doi.org/10.1109/ICCISci.2019.8716382
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	T Technology (General)
spellingShingle	T Technology (General) Muhammad, Badaruddin Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Mohamad, Mohd. Saberi Mohd. Azmi, Kamil Zakwan Mat Jusof, Mohd. Falfazli Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter
description	Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning with a jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases.
format	Conference or Workshop Item
author	Muhammad, Badaruddin Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Mohamad, Mohd. Saberi Mohd. Azmi, Kamil Zakwan Mat Jusof, Mohd. Falfazli
author_facet	Muhammad, Badaruddin Ibrahim, Zuwairie Shapiai, Mohd. Ibrahim Mohamad, Mohd. Saberi Mohd. Azmi, Kamil Zakwan Mat Jusof, Mohd. Falfazli
author_sort	Muhammad, Badaruddin
title	Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter
title_short	Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter
title_full	Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter
title_fullStr	Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter
title_full_unstemmed	Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter
title_sort	oppositional learning prediction operator with jumping rate for simulated kalman filter
publishDate	2019
url	http://eprints.utm.my/id/eprint/97106/ http://dx.doi.org/10.1109/ICCISci.2019.8716382
_version_	1745562341681922048
score	13.2014675

Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter

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