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Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems

Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. S...

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Main Authors: Zuwairie, Ibrahim, Zulkifli, Md. Yusof, Asrul, Adam, Kamil Zakwan, Mohd Azmi, Tasiransurini, Ab Rahman, Badaruddin, Muhammad, Nor Azlina, Ab. Aziz, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Mohd Saberi, Mohamad
Format: Article
Language:English
English
Published: Science Publishing Corporation Inc. 2018
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/22970/1/79.%20Distance%20evaluated%20simulated%20kalman%20filter%20with%20state.pdf
http://umpir.ump.edu.my/id/eprint/22970/2/79.1%20Distance%20evaluated%20simulated%20kalman%20filter%20with%20state.pdf
http://umpir.ump.edu.my/id/eprint/22970/
https://doi.org/10.14419/ijet.v7i4.27.22431
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spelling my.ump.umpir.229702018-12-27T01:22:00Z http://umpir.ump.edu.my/id/eprint/22970/ Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems Zuwairie, Ibrahim Zulkifli, Md. Yusof Asrul, Adam Kamil Zakwan, Mohd Azmi Tasiransurini, Ab Rahman Badaruddin, Muhammad Nor Azlina, Ab. Aziz Norrima, Mokhtar Mohd Ibrahim, Shapiai Mohd Saberi, Mohamad T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algo-rithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algo-rithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combi-natorial SKF algorithm based on a set of Traveling Salesman Problem (TSP). Science Publishing Corporation Inc. 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22970/1/79.%20Distance%20evaluated%20simulated%20kalman%20filter%20with%20state.pdf pdf en http://umpir.ump.edu.my/id/eprint/22970/2/79.1%20Distance%20evaluated%20simulated%20kalman%20filter%20with%20state.pdf Zuwairie, Ibrahim and Zulkifli, Md. Yusof and Asrul, Adam and Kamil Zakwan, Mohd Azmi and Tasiransurini, Ab Rahman and Badaruddin, Muhammad and Nor Azlina, Ab. Aziz and Norrima, Mokhtar and Mohd Ibrahim, Shapiai and Mohd Saberi, Mohamad (2018) Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems. International Journal of Engineering and Technology(UAE), 7 (4). pp. 22-29. ISSN 2227524X https://doi.org/10.14419/ijet.v7i4.27.22431
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Zuwairie, Ibrahim
Zulkifli, Md. Yusof
Asrul, Adam
Kamil Zakwan, Mohd Azmi
Tasiransurini, Ab Rahman
Badaruddin, Muhammad
Nor Azlina, Ab. Aziz
Norrima, Mokhtar
Mohd Ibrahim, Shapiai
Mohd Saberi, Mohamad
Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
description Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algo-rithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algo-rithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combi-natorial SKF algorithm based on a set of Traveling Salesman Problem (TSP).
format Article
author Zuwairie, Ibrahim
Zulkifli, Md. Yusof
Asrul, Adam
Kamil Zakwan, Mohd Azmi
Tasiransurini, Ab Rahman
Badaruddin, Muhammad
Nor Azlina, Ab. Aziz
Norrima, Mokhtar
Mohd Ibrahim, Shapiai
Mohd Saberi, Mohamad
author_facet Zuwairie, Ibrahim
Zulkifli, Md. Yusof
Asrul, Adam
Kamil Zakwan, Mohd Azmi
Tasiransurini, Ab Rahman
Badaruddin, Muhammad
Nor Azlina, Ab. Aziz
Norrima, Mokhtar
Mohd Ibrahim, Shapiai
Mohd Saberi, Mohamad
author_sort Zuwairie, Ibrahim
title Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
title_short Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
title_full Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
title_fullStr Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
title_full_unstemmed Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
title_sort distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
publisher Science Publishing Corporation Inc.
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/22970/1/79.%20Distance%20evaluated%20simulated%20kalman%20filter%20with%20state.pdf
http://umpir.ump.edu.my/id/eprint/22970/2/79.1%20Distance%20evaluated%20simulated%20kalman%20filter%20with%20state.pdf
http://umpir.ump.edu.my/id/eprint/22970/
https://doi.org/10.14419/ijet.v7i4.27.22431
_version_ 1643669486491926528
score 13.211869

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