Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm

Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm

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, Abd Aziz, Nor Azlina, Ab. Aziz, Saifudin, Razali, Mohd Saberi, Mohamad
Format: Conference or Workshop Item
Language:English
English
Published: Advanced Science Letters 2016
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/12020/
http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf
http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf
id ump-12020
recordtype eprints
spelling ump-120202018-02-08T03:03:58Z http://umpir.ump.edu.my/id/eprint/12020/ Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm Zuwairie, Ibrahim Nor Hidayati, Abd Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Saberi, Mohamad TK Electrical engineering. Electronics Nuclear engineering 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. An 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 Simulated Kalman Filter algorithm, it is applied to 30 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, and has a comparable performance to some well-known metaheuristic algorithms. Advanced Science Letters 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf Zuwairie, Ibrahim and Nor Hidayati, Abd Aziz and Nor Azlina, Ab. Aziz and Saifudin, Razali and Mohd Saberi, Mohamad (2016) Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm. In: International Symposium of Information and Internet Technology, 26-28 January 2016 , Melaka. . (In Press)
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
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zuwairie, Ibrahim
Nor Hidayati, Abd Aziz
Nor Azlina, Ab. Aziz
Saifudin, Razali
Mohd Saberi, Mohamad
Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
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. An 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 Simulated Kalman Filter algorithm, it is applied to 30 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, and has a comparable performance to some well-known metaheuristic algorithms.
format Conference or Workshop Item
author Zuwairie, Ibrahim
Nor Hidayati, Abd Aziz
Nor Azlina, Ab. Aziz
Saifudin, Razali
Mohd Saberi, Mohamad
author_facet Zuwairie, Ibrahim
Nor Hidayati, Abd Aziz
Nor Azlina, Ab. Aziz
Saifudin, Razali
Mohd Saberi, Mohamad
author_sort Zuwairie, Ibrahim
title Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
title_short Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
title_full Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
title_fullStr Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
title_full_unstemmed Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
title_sort simulated kalman filter: a novel estimation-based metaheuristic optimization algorithm
publisher Advanced Science Letters
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/12020/
http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf
http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf
first_indexed 2023-09-18T22:13:11Z
last_indexed 2023-09-18T22:13:11Z
_version_ 1777415164204679168

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