Single-agent finite impulse response optimizer for numerical optimization problems

This paper introduces a new single-agent metaheuristic optimization algorithm, named single-agent ﬁnite impulse response optimizer (SAFIRO). This proposed algorithm is inspired by the estimation ability of the ultimate iterative unbiased ﬁnite impulse response (UFIR) ﬁlter. The UFIR ﬁlter is one of...

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Main Authors:	Tasiransurini, Ab Rahman, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Zhao, Shunyi, Nor Hidayati, Abdul Aziz
Format:	Article
Language:	English
Published:	IEEE 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/24062/ http://umpir.ump.edu.my/id/eprint/24062/ http://umpir.ump.edu.my/id/eprint/24062/ http://umpir.ump.edu.my/id/eprint/24062/7/Single-Agent%20Finite%20Impulse%20Response.pdf

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recordtype	eprints
spelling	ump-240622019-03-13T04:57:50Z http://umpir.ump.edu.my/id/eprint/24062/ Single-agent finite impulse response optimizer for numerical optimization problems Tasiransurini, Ab Rahman Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Zhao, Shunyi Nor Hidayati, Abdul Aziz QA75 Electronic computers. Computer science This paper introduces a new single-agent metaheuristic optimization algorithm, named single-agent ﬁnite impulse response optimizer (SAFIRO). This proposed algorithm is inspired by the estimation ability of the ultimate iterative unbiased ﬁnite impulse response (UFIR) ﬁlter. The UFIR ﬁlter is one of the variants of the ﬁnite impulse response (FIR) ﬁlter, whereby in state space models, the FIR ﬁlter can be used as an option other than the Kalman ﬁlter (KF) for state estimation. Unlike the KF, the UFIR ﬁlter does not require any noise covariance, error covariance, and initial condition to calculate the state estimate. The UFIR ﬁlter also provides an iterative Kalman-like form to improve the estimation process. In the SAFIRO algorithm, the agent works as an individual UFIR to ﬁnd an optimal or a near-optimal solution, where the agent needs to perform two main tasks; measurement and estimation. The performance of the SAFIRO algorithm is evaluated using the CEC 2014 Benchmark Test Suite for single-objective optimization and statistically compared with the several well-known metaheuristic optimization algorithms, such as Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimization algorithm. The experimental results show that the proposed SAFIRO algorithm is able to converge to the optimal and the near-optimal solutions, and signiﬁcantly outperform all the aforementioned state-of-the-art metaheuristic algorithms. IEEE 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24062/7/Single-Agent%20Finite%20Impulse%20Response.pdf Tasiransurini, Ab Rahman and Zuwairie, Ibrahim and Nor Azlina, Ab. Aziz and Zhao, Shunyi and Nor Hidayati, Abdul Aziz (2018) Single-agent finite impulse response optimizer for numerical optimization problems. IEEE Access, 6. pp. 9358-9374. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2017.2777894 https://doi.org/10.1109/ACCESS.2017.2777894
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 Tasiransurini, Ab Rahman Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Zhao, Shunyi Nor Hidayati, Abdul Aziz Single-agent finite impulse response optimizer for numerical optimization problems
description	This paper introduces a new single-agent metaheuristic optimization algorithm, named single-agent ﬁnite impulse response optimizer (SAFIRO). This proposed algorithm is inspired by the estimation ability of the ultimate iterative unbiased ﬁnite impulse response (UFIR) ﬁlter. The UFIR ﬁlter is one of the variants of the ﬁnite impulse response (FIR) ﬁlter, whereby in state space models, the FIR ﬁlter can be used as an option other than the Kalman ﬁlter (KF) for state estimation. Unlike the KF, the UFIR ﬁlter does not require any noise covariance, error covariance, and initial condition to calculate the state estimate. The UFIR ﬁlter also provides an iterative Kalman-like form to improve the estimation process. In the SAFIRO algorithm, the agent works as an individual UFIR to ﬁnd an optimal or a near-optimal solution, where the agent needs to perform two main tasks; measurement and estimation. The performance of the SAFIRO algorithm is evaluated using the CEC 2014 Benchmark Test Suite for single-objective optimization and statistically compared with the several well-known metaheuristic optimization algorithms, such as Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimization algorithm. The experimental results show that the proposed SAFIRO algorithm is able to converge to the optimal and the near-optimal solutions, and signiﬁcantly outperform all the aforementioned state-of-the-art metaheuristic algorithms.
format	Article
author	Tasiransurini, Ab Rahman Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Zhao, Shunyi Nor Hidayati, Abdul Aziz
author_facet	Tasiransurini, Ab Rahman Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Zhao, Shunyi Nor Hidayati, Abdul Aziz
author_sort	Tasiransurini, Ab Rahman
title	Single-agent finite impulse response optimizer for numerical optimization problems
title_short	Single-agent finite impulse response optimizer for numerical optimization problems
title_full	Single-agent finite impulse response optimizer for numerical optimization problems
title_fullStr	Single-agent finite impulse response optimizer for numerical optimization problems
title_full_unstemmed	Single-agent finite impulse response optimizer for numerical optimization problems
title_sort	single-agent finite impulse response optimizer for numerical optimization problems
publisher	IEEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/24062/ http://umpir.ump.edu.my/id/eprint/24062/ http://umpir.ump.edu.my/id/eprint/24062/ http://umpir.ump.edu.my/id/eprint/24062/7/Single-Agent%20Finite%20Impulse%20Response.pdf
first_indexed	2023-09-18T22:36:15Z
last_indexed	2023-09-18T22:36:15Z
_version_	1777416615018627072

Single-agent finite impulse response optimizer for numerical optimization problems

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