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:	Badaruddin, Muhammad, Mohd Saberi, Mohamad, Zuwairie, Ibrahim, Kamil Zakwan, Mohd Azmi, Mohd Ibrahim, Shapiai, Mohd Falfazli, Mat Jusof
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/25147/ http://umpir.ump.edu.my/id/eprint/25147/ http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf

id	ump-25147
recordtype	eprints
spelling	ump-251472019-11-11T04:46:54Z http://umpir.ump.edu.my/id/eprint/25147/ Oppositional learning prediction operator with jumping rate for simulated kalman filter Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof TK Electrical engineering. Electronics Nuclear engineering TS Manufactures 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 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. IEEE 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf pdf en http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf Badaruddin, Muhammad and Mohd Saberi, Mohamad and Zuwairie, Ibrahim and Kamil Zakwan, Mohd Azmi and Mohd Ibrahim, Shapiai and Mohd Falfazli, Mat Jusof (2019) Oppositional learning prediction operator with jumping rate for simulated kalman filter. In: International Conference on Computer and Information Sciences, ICCIS 2019, 3 - 4 April 2019 , Jouf University, Aljouf, Kingdom of Saudi Arabia. pp. 1-7.. ISBN 978-153868125-1 https://doi.org/10.1109/ICCISci.2019.8716382
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof 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 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	Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof
author_facet	Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof
author_sort	Badaruddin, Muhammad
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
publisher	IEEE
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25147/ http://umpir.ump.edu.my/id/eprint/25147/ http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf
first_indexed	2023-09-18T22:38:28Z
last_indexed	2023-09-18T22:38:28Z
_version_	1777416754304122880

Oppositional learning prediction operator with jumping rate for simulated kalman filter

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