Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems
Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agen...
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Online Access: | http://umpir.ump.edu.my/id/eprint/11530/1/Angle%20Modulated%20Simulated%20Kalman%20Filter%20Algorithm%20for%20Combinatorial%20Optimization%20Problems.pdf http://umpir.ump.edu.my/id/eprint/11530/7/fkee-2016-zul-Angle%20Modulated%20Simulated%20Kalman%20Filter%20Algorithm1.pdf http://umpir.ump.edu.my/id/eprint/11530/ http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0416_4036.pdf |
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my.ump.umpir.115302017-10-30T02:30:31Z http://umpir.ump.edu.my/id/eprint/11530/ Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems Zulkifli, Md. Yusof Zuwairie, Ibrahim Ismail, Ibrahim Kamil Zakwan, Mohd Azmi Nor Azlina, Ab. Aziz Nor Hidayati, Abd. Aziz Mohd Saberi, Mohamad TK Electrical engineering. Electronics Nuclear engineering Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum.Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, the SKF algorithm is combined with an angle modulated approach. The performance of the proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms,namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA). A set of traveling salesman problems are used to evaluate the performance of the proposed AMSKF. Based on the analysis of experimental results, we found that the proposed AMSKF is as competitive as BGSA but the BPSO is superior than the both AMSKF and BGSA Asian Research Publishing Network (ARPN) 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/11530/1/Angle%20Modulated%20Simulated%20Kalman%20Filter%20Algorithm%20for%20Combinatorial%20Optimization%20Problems.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/11530/7/fkee-2016-zul-Angle%20Modulated%20Simulated%20Kalman%20Filter%20Algorithm1.pdf Zulkifli, Md. Yusof and Zuwairie, Ibrahim and Ismail, Ibrahim and Kamil Zakwan, Mohd Azmi and Nor Azlina, Ab. Aziz and Nor Hidayati, Abd. Aziz and Mohd Saberi, Mohamad (2016) Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems. ARPN Journal of Engineering and Applied Sciences, 11 (7). 4854 -4859. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0416_4036.pdf |
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TK Electrical engineering. Electronics Nuclear engineering Zulkifli, Md. Yusof Zuwairie, Ibrahim Ismail, Ibrahim Kamil Zakwan, Mohd Azmi Nor Azlina, Ab. Aziz Nor Hidayati, Abd. Aziz Mohd Saberi, Mohamad Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems |
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Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based
optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum.Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, the SKF algorithm is combined with an angle modulated approach. The performance of the proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms,namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA). A set of traveling salesman problems are used to evaluate the performance of the proposed AMSKF. Based on the analysis of experimental results, we found that the proposed AMSKF is as competitive as BGSA but the BPSO is superior than the both AMSKF and BGSA |
format |
Article |
author |
Zulkifli, Md. Yusof Zuwairie, Ibrahim Ismail, Ibrahim Kamil Zakwan, Mohd Azmi Nor Azlina, Ab. Aziz Nor Hidayati, Abd. Aziz Mohd Saberi, Mohamad |
author_facet |
Zulkifli, Md. Yusof Zuwairie, Ibrahim Ismail, Ibrahim Kamil Zakwan, Mohd Azmi Nor Azlina, Ab. Aziz Nor Hidayati, Abd. Aziz Mohd Saberi, Mohamad |
author_sort |
Zulkifli, Md. Yusof |
title |
Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems |
title_short |
Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems |
title_full |
Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems |
title_fullStr |
Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems |
title_full_unstemmed |
Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems |
title_sort |
angle modulated simulated kalman filter algorithm for combinatorial optimization problems |
publisher |
Asian Research Publishing Network (ARPN) |
publishDate |
2016 |
url |
http://umpir.ump.edu.my/id/eprint/11530/1/Angle%20Modulated%20Simulated%20Kalman%20Filter%20Algorithm%20for%20Combinatorial%20Optimization%20Problems.pdf http://umpir.ump.edu.my/id/eprint/11530/7/fkee-2016-zul-Angle%20Modulated%20Simulated%20Kalman%20Filter%20Algorithm1.pdf http://umpir.ump.edu.my/id/eprint/11530/ http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0416_4036.pdf |
_version_ |
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