Enhancing simulated kalman filter algorithm using current optimum opposition-based learning

Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search...

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Main Authors:	Kamil Zakwan, Mohd Azmi, Zuwairie, Ibrahim, Pebrianti, Dwi, Mohd Falfazli, Mat Jusof, Nor Hidayati, Abdul Aziz, Nor Azlina, Ab. Aziz
Format:	Article
Language:	English
Published:	Penerbit UMP 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/24720/ http://umpir.ump.edu.my/id/eprint/24720/ http://umpir.ump.edu.my/id/eprint/24720/ http://umpir.ump.edu.my/id/eprint/24720/8/Enhancing%20simulated%20Kalman%20filter%20algorithm%20using%20current.pdf

id	ump-24720
recordtype	eprints
spelling	ump-247202019-11-21T04:38:50Z http://umpir.ump.edu.my/id/eprint/24720/ Enhancing simulated kalman filter algorithm using current optimum opposition-based learning Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE congress on evolutionary computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all. Penerbit UMP 2019 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/24720/8/Enhancing%20simulated%20Kalman%20filter%20algorithm%20using%20current.pdf Kamil Zakwan, Mohd Azmi and Zuwairie, Ibrahim and Pebrianti, Dwi and Mohd Falfazli, Mat Jusof and Nor Hidayati, Abdul Aziz and Nor Azlina, Ab. Aziz (2019) Enhancing simulated kalman filter algorithm using current optimum opposition-based learning. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 1 (1). pp. 1-13. ISSN 2637-0883 http://journal.ump.edu.my/mekatronika/article/view/157 https://doi.org/10.15282/mekatronika.v1i1.157
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
description	Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE congress on evolutionary computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all.
format	Article
author	Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz
author_facet	Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz
author_sort	Kamil Zakwan, Mohd Azmi
title	Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
title_short	Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
title_full	Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
title_fullStr	Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
title_full_unstemmed	Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
title_sort	enhancing simulated kalman filter algorithm using current optimum opposition-based learning
publisher	Penerbit UMP
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24720/ http://umpir.ump.edu.my/id/eprint/24720/ http://umpir.ump.edu.my/id/eprint/24720/ http://umpir.ump.edu.my/id/eprint/24720/8/Enhancing%20simulated%20Kalman%20filter%20algorithm%20using%20current.pdf
first_indexed	2023-09-18T22:37:35Z
last_indexed	2023-09-18T22:37:35Z
_version_	1777416698940358656

Enhancing simulated kalman filter algorithm using current optimum opposition-based learning

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