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Four Different Methods to Hybrid Simulated Kalman Filter (SKF) with Gravitational Search Algorithm (GSA)

Badaruddin, Muhammad and Zuwairie, Ibrahim and Kamil Zakwan, Mohd Azmi and Khairul Hamimah, Abas and Nor Azlina, Ab. Aziz and Nor Hidayati, Abd Aziz and Mohd Saberi, Mohamad (2016) Four Different Methods to Hybrid Simulated Kalman Filter (SKF) with Gravitational Search Algorithm (GSA). In: Proceedings of The National Conference for Postgraduate Research (NCON-PGR 2016), 24-25 September 2016 , Universiti Malaysia Pahang (UMP), Pekan, Pahang. pp. 854-864..

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Abstract

This paper presents a performance evaluation of a new hybrid Simulated Kalman Filter and Gravitational Algorithm (SKF-GSA), for continuous numerical optimization problems. Simulated Kalman filter (SKF) was inspired by the estimation capability of Kalman filter. Every agent in SKF is regarded as a Kalman filter. Inspired by the Newtonian gravitational law,gravitational search algorithm (GSA) has been introduced in 2009. Four methods (models) to hybridize SKF and GSA are proposed in this paper. The performance of the hybrid SKF-GSA algorithms is compared against the original SKF using CEC2014 benchmark dataset for continuous numerical optimization problems. Based on the analysis of experimental results, we found that model 3 and model 4 are performed better than the original SKF.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Simulated Kalman Filter, Hybrid Simulated Kalman Filter, Continuous Numerical Optimization Problems
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Rosfadilla Mohamad Zainun
Date Deposited: 09 Dec 2016 06:10
Last Modified: 22 Aug 2017 07:09
URI: http://umpir.ump.edu.my/id/eprint/15725
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