Opposition-based learning simulated kalman filter for Numerical optimization problems

Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. Th...

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Main Author: Mohd Falfazli, Mat Jusof
Format: Research Book Profile
Language:English
Published: 2016
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Online Access:http://umpir.ump.edu.my/id/eprint/36251/1/Opposition-based%20learning%20simulated%20kalman%20filter%20for%20Numerical%20optimization%20problems.pdf
http://umpir.ump.edu.my/id/eprint/36251/
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Institution: Universiti Malaysia Pahang Al-Sultan Abdullah
Language: English
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spelling my.ump.umpir.362512023-01-04T02:12:31Z http://umpir.ump.edu.my/id/eprint/36251/ Opposition-based learning simulated kalman filter for Numerical optimization problems Mohd Falfazli, Mat Jusof TK Electrical engineering. Electronics Nuclear engineering Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. The opposition-based learning can be applied either after the solution is updated or as the prediction step in SKF. Using CEC2014 benchmark suite, it is found that the SKF with opposition-based learning outperforms the original SKF algorithm in most cases. The SKF with opposition-based learning is also applied as adaptive beamforming algorithm for adaptive array antenna. In this application, the objective is to maximize the signal to interference plus noise ratio (SINR) and results show that the SKF with opposition-based learning outperforms the existing adaptive mutated Boolean particle swarm optimization (AMBPSO) 2016 Research Book Profile NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36251/1/Opposition-based%20learning%20simulated%20kalman%20filter%20for%20Numerical%20optimization%20problems.pdf Mohd Falfazli, Mat Jusof (2016) Opposition-based learning simulated kalman filter for Numerical optimization problems. , [Research Book Profile: Research Report] (Unpublished)
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mohd Falfazli, Mat Jusof
Opposition-based learning simulated kalman filter for Numerical optimization problems
description Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. The opposition-based learning can be applied either after the solution is updated or as the prediction step in SKF. Using CEC2014 benchmark suite, it is found that the SKF with opposition-based learning outperforms the original SKF algorithm in most cases. The SKF with opposition-based learning is also applied as adaptive beamforming algorithm for adaptive array antenna. In this application, the objective is to maximize the signal to interference plus noise ratio (SINR) and results show that the SKF with opposition-based learning outperforms the existing adaptive mutated Boolean particle swarm optimization (AMBPSO)
format Research Book Profile
author Mohd Falfazli, Mat Jusof
author_facet Mohd Falfazli, Mat Jusof
author_sort Mohd Falfazli, Mat Jusof
title Opposition-based learning simulated kalman filter for Numerical optimization problems
title_short Opposition-based learning simulated kalman filter for Numerical optimization problems
title_full Opposition-based learning simulated kalman filter for Numerical optimization problems
title_fullStr Opposition-based learning simulated kalman filter for Numerical optimization problems
title_full_unstemmed Opposition-based learning simulated kalman filter for Numerical optimization problems
title_sort opposition-based learning simulated kalman filter for numerical optimization problems
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/36251/1/Opposition-based%20learning%20simulated%20kalman%20filter%20for%20Numerical%20optimization%20problems.pdf
http://umpir.ump.edu.my/id/eprint/36251/
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