A Kalman Filter Approach for Solving Unimodal Optimization Problems

In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each age...

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Main Authors:	Zuwairie, Ibrahim, Nor Hidayati, Abdul Aziz, Nor Azlina, Ab. Aziz, Saifudin, Razali, Mohd Ibrahim, Shapiai, Sophan Wahyudi, Nawawi, Mohd Saberi, Mohamad
Format:	Article
Language:	English
Published:	ICIC International 2015
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/19724/ http://umpir.ump.edu.my/id/eprint/19724/ http://umpir.ump.edu.my/id/eprint/19724/1/fkee-2015-zuwairie-a%20kalman%20filter%20approach%20for%20solving.pdf

id	ump-19724
recordtype	eprints
spelling	ump-197242018-02-02T02:32:58Z http://umpir.ump.edu.my/id/eprint/19724/ A Kalman Filter Approach for Solving Unimodal Optimization Problems Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad QA75 Electronic computers. Computer science In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each agent in SKF acts as a Kalman Filter. Every agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. To evaluate the performance of the SKF algorithm in solving unimodal optimization problems, it is applied unimodal benchmark functions of CEC 2014 for real-parameter single objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach in solving unimodal optimization problems and has a comparable performance to some well-known metaheuristic algorithms. ICIC International 2015-12 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19724/1/fkee-2015-zuwairie-a%20kalman%20filter%20approach%20for%20solving.pdf Zuwairie, Ibrahim and Nor Hidayati, Abdul Aziz and Nor Azlina, Ab. Aziz and Saifudin, Razali and Mohd Ibrahim, Shapiai and Sophan Wahyudi, Nawawi and Mohd Saberi, Mohamad (2015) A Kalman Filter Approach for Solving Unimodal Optimization Problems. ICIC Express Letters, 9 (12). pp. 3415-3422. ISSN 1881-803X http://www.ijicic.org/el-9(12).htm
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad A Kalman Filter Approach for Solving Unimodal Optimization Problems
description	In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each agent in SKF acts as a Kalman Filter. Every agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. To evaluate the performance of the SKF algorithm in solving unimodal optimization problems, it is applied unimodal benchmark functions of CEC 2014 for real-parameter single objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach in solving unimodal optimization problems and has a comparable performance to some well-known metaheuristic algorithms.
format	Article
author	Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad
author_facet	Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad
author_sort	Zuwairie, Ibrahim
title	A Kalman Filter Approach for Solving Unimodal Optimization Problems
title_short	A Kalman Filter Approach for Solving Unimodal Optimization Problems
title_full	A Kalman Filter Approach for Solving Unimodal Optimization Problems
title_fullStr	A Kalman Filter Approach for Solving Unimodal Optimization Problems
title_full_unstemmed	A Kalman Filter Approach for Solving Unimodal Optimization Problems
title_sort	kalman filter approach for solving unimodal optimization problems
publisher	ICIC International
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/19724/ http://umpir.ump.edu.my/id/eprint/19724/ http://umpir.ump.edu.my/id/eprint/19724/1/fkee-2015-zuwairie-a%20kalman%20filter%20approach%20for%20solving.pdf
first_indexed	2023-09-18T22:28:15Z
last_indexed	2023-09-18T22:28:15Z
_version_	1777416112403644416

A Kalman Filter Approach for Solving Unimodal Optimization Problems

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