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Oppositional learning prediction operator with jumping rate for simulated kalman filter

Badaruddin, Muhammad and Mohd Saberi, Mohamad and Zuwairie, Ibrahim and Kamil Zakwan, Mohd Azmi and Mohd Ibrahim, Shapiai and Mohd Falfazli, Mat Jusof (2019) Oppositional learning prediction operator with jumping rate for simulated kalman filter. In: International Conference on Computer and Information Sciences, ICCIS 2019, 3 - 4 April 2019 , Jouf University, Aljouf, Kingdom of Saudi Arabia. pp. 1-7.. ISBN 978-153868125-1

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Abstract

Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning with jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Optimization; Simulated Kalman filter; Pre-mature convergences
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TS Manufactures
Faculty/Division: Faculty of Electrical & Electronic Engineering
Faculty of Manufacturing Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 11 Nov 2019 04:46
Last Modified: 11 Nov 2019 04:46
URI: http://umpir.ump.edu.my/id/eprint/25147
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