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Performance Evaluation of Hybrid SKF Algorithms: Hybrid SKF-PSO and Hybrid SKF-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) Performance Evaluation of Hybrid SKF Algorithms: Hybrid SKF-PSO and Hybrid SKF-GSA. In: National Conference For Postgraduate Research (NCON-PGR 2016), 24-25 September 2016 , Universiti Malaysia Pahang, Pekan. pp. 865-874..

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Abstract

This paper presents a performance evaluation of hybrid Simulated Kalman Filter Gravitational Algorithm (SKF-GSA), and hybrid Simulated Kalman Filter Particle Swarm Optimization (SKF-PSO), 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. The performance of the hybrid algorithms (SKF-GSA and SKF-PSO) is compared using CEC2014 benchmark dataset for continuous numerical optimization problems. Based on the analysis of experimental results, we found that the SKF-PSO performs the best among all.

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 05:59
Last Modified: 22 Aug 2017 07:09
URI: http://umpir.ump.edu.my/id/eprint/15724
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