An Oppositional Learning Prediction Operator for Simulated Kalman Filter


Ab Aziz, Nor Azlina and Abdul Aziz, Nor Hidayati and Ibrahim, Zuwairie and Mohd Azmi, Kamil Zakwan and Muhammad, Badaruddin and Mat Jusof, Mohd Falfazli and Shapia, Mohd Ibrahim (2019) An Oppositional Learning Prediction Operator for Simulated Kalman Filter. In: 3rd International Conference on Computational Intelligence and Applications, ICCIA 2018, 28-30 July 2018, Hong Kong, China.

[img] Text
153.pdf - Published Version
Restricted to Repository staff only

Download (280kB)


Simulated Kalman filter (SKF) is a recent metaheuristic optimization algorithm established in 2015. In the present 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. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator outperforms the original SKF algorithm in most cases.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Simulated Kalman filter
Subjects: Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 26 Jan 2022 03:40
Last Modified: 26 Jan 2022 03:40


Downloads per month over past year

View ItemEdit (login required)