A multiobjective simulated Kalman filter optimization algorithm

A multiobjective simulated Kalman filter optimization algorithm

This paper presents a new multiobjective type optimization algorithm known as a Multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) a...

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Main Authors: A. Azwan, A. Razak, Mohd Falfazli, Mat Jusof, Ahmad Nor Kasruddin, Nasir, Mohd Ashraf, Ahmad
Format: Conference or Workshop Item
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/22962/
http://umpir.ump.edu.my/id/eprint/22962/
http://umpir.ump.edu.my/id/eprint/22962/1/A%20multiobjective%20simulated%20Kalman%20filter%20optimization%20algorithm.pdf
id ump-22962
recordtype eprints
spelling ump-229622018-12-24T08:03:58Z http://umpir.ump.edu.my/id/eprint/22962/ A multiobjective simulated Kalman filter optimization algorithm A. Azwan, A. Razak Mohd Falfazli, Mat Jusof Ahmad Nor Kasruddin, Nasir Mohd Ashraf, Ahmad TK Electrical engineering. Electronics Nuclear engineering This paper presents a new multiobjective type optimization algorithm known as a Multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) approach is introduced to formulate the multiobjective type algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. A Non-dominated Solution (NS) approach is utilized in the formulation of the multiobjective strategy. Cost function value and diversity spacing parameters are taken into consideration in the strategy. Every single agent carries those two parameters in which will be used to compare with other solutions from other agents in order to determine its domination. A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. The algorithm is tested with various multiobjective benchmark functions and compared with Non-Dominated Sorting Genetic Algorithm 2 (NSGA2) multiobjective algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms NSGA2 significantly. The result also is presented in a graphical form to compare the generated Pareto solution based on proposed MOSKF and original NSGA2 with the theoretical Pareto solution. Institute of Electrical and Electronics Engineers Inc. 2018-06 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22962/1/A%20multiobjective%20simulated%20Kalman%20filter%20optimization%20algorithm.pdf A. Azwan, A. Razak and Mohd Falfazli, Mat Jusof and Ahmad Nor Kasruddin, Nasir and Mohd Ashraf, Ahmad (2018) A multiobjective simulated Kalman filter optimization algorithm. In: Proceedings of 4th IEEE International Conference on Applied System Innovation 2018: 4th IEEE International Conference on Applied System Innovation, 13-17 April 2018 , Chiba, Tokyo. pp. 23-26.. ISBN 978-153864342-6 https://doi.org/10.1109/ICASI.2018.8394257
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
A. Azwan, A. Razak
Mohd Falfazli, Mat Jusof
Ahmad Nor Kasruddin, Nasir
Mohd Ashraf, Ahmad
A multiobjective simulated Kalman filter optimization algorithm
description This paper presents a new multiobjective type optimization algorithm known as a Multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) approach is introduced to formulate the multiobjective type algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. A Non-dominated Solution (NS) approach is utilized in the formulation of the multiobjective strategy. Cost function value and diversity spacing parameters are taken into consideration in the strategy. Every single agent carries those two parameters in which will be used to compare with other solutions from other agents in order to determine its domination. A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. The algorithm is tested with various multiobjective benchmark functions and compared with Non-Dominated Sorting Genetic Algorithm 2 (NSGA2) multiobjective algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms NSGA2 significantly. The result also is presented in a graphical form to compare the generated Pareto solution based on proposed MOSKF and original NSGA2 with the theoretical Pareto solution.
format Conference or Workshop Item
author A. Azwan, A. Razak
Mohd Falfazli, Mat Jusof
Ahmad Nor Kasruddin, Nasir
Mohd Ashraf, Ahmad
author_facet A. Azwan, A. Razak
Mohd Falfazli, Mat Jusof
Ahmad Nor Kasruddin, Nasir
Mohd Ashraf, Ahmad
author_sort A. Azwan, A. Razak
title A multiobjective simulated Kalman filter optimization algorithm
title_short A multiobjective simulated Kalman filter optimization algorithm
title_full A multiobjective simulated Kalman filter optimization algorithm
title_fullStr A multiobjective simulated Kalman filter optimization algorithm
title_full_unstemmed A multiobjective simulated Kalman filter optimization algorithm
title_sort multiobjective simulated kalman filter optimization algorithm
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/22962/
http://umpir.ump.edu.my/id/eprint/22962/
http://umpir.ump.edu.my/id/eprint/22962/1/A%20multiobjective%20simulated%20Kalman%20filter%20optimization%20algorithm.pdf
first_indexed 2023-09-18T22:34:11Z
last_indexed 2023-09-18T22:34:11Z
_version_ 1777416485600231424

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