Muhammad, Badaruddin and Ibrahim, Zuwairie and Shapiai, Mohd. Ibrahim and Mohamad, Mohd. Saberi and Mohd. Azmi, Kamil Zakwan and Mat Jusof, Mohd. Falfazli (2019) Oppositional learning prediction operator with jumping rate for Simulated Kalman Filter. In: 2019 International Conference on Computer and Information Sciences, ICCIS 2019, 3 - 4 April 2019, Sakaka, Saudi Arabia.
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Official URL: http://dx.doi.org/10.1109/ICCISci.2019.8716382
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 a 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 (Paper) |
|---|---|
| Uncontrolled Keywords: | Optimization, Simulated Kalman filter |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Malaysia-Japan International Institute of Technology |
| ID Code: | 97106 |
| Deposited By: | Widya Wahid |
| Deposited On: | 23 Sep 2022 01:22 |
| Last Modified: | 23 Sep 2022 01:22 |
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