A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems

Badaruddin, Muhammad and Zuwairie, Ibrahim and Kamarul Hawari, Ghazali and Kamil Zakwan, Mohd Azmi and Nor Azlina, Ab. Aziz and Nor Hidayati, Abd Aziz and Mohd Saberi, Mohamad (2015) A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems. In: International Conference on Electrical Control and Computer Engineering 2015 , 27-28 Oct 2015 , Kuantan, Pahang. . (Unpublished)

[img] PDF
A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems.pdf
Restricted to Repository staff only

Download (943kB) | Request a copy
[img]
Preview
PDF
A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems-abstract.pdf

Download (124kB) | Preview

Abstract

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel population-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, which includes prediction, measurement, and estimation, the global minimum/maximum can be estimated. Measurement process, 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.Inspired by the bird flocking, particle swarm optimization (PSO) has been introduced in 1994. In PSO, a swarm of agent search the global minimum/maximum by velocity and position updates, which are influenced by current position of agent,current position of agent, personal best, and global best of the swarm. In this research, SKF and PSO are hybridized in such a way that PSO is employed as prediction operator in SKF. The performance of the proposed hybrid SKF-PSO algorithm (SKF-PSO) is compared against SKF and PSO using CEC2014 benchmark dataset for continuous numerical optimization problems. Based on the analysis of experimental results, we found that the proposed hybrid SKF-PSO is superior than both SKF and PSO algorithm

Item Type: Conference or Workshop Item (Speech)
Uncontrolled Keywords: Simulated Kalman Filter, Particle Swarm Optimization, CEC2014 Benchmark Problem
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 03 Dec 2015 02:59
Last Modified: 02 Feb 2018 03:15
URI: http://umpir.ump.edu.my/id/eprint/11528
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item