Abstract
This paper presents an improved Simulated Kalman Filter optimization algorithm. It is a further enhancement of a Simulated Kalman Filter (SKF) optimization algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. An exponential term is introduced into Estimation stages of SKF to speed up the searching process and gain more chances in finding better solutions. Cost function value that represents an accuracy of a solution is considered as the ultimate goal. Every single agent carries an information about the accuracy of a solution in which will be used to compare with other solutions from other agents. A solution that has a lower cost function is considered as the best solution. The algorithm is tested with various benchmark functions and compared with the original SKF algorithm. The result of the analysis on the accuracy tested on the benchmark functions shows that the proposed algorithm outperforms SKF significantly. Graphical results of the test show that the SKFIA has faster convergence speed as compared to SKF.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ibrahim, Z., Aziz, N.H.A., Aziz, N.N.A., Razali, S., Shapiai, M.I., Nawawi, S.W., Mohamad, M.S.: A Kalman filter approach for solving unimodal optimization problems. ICIC Express Lett. 9(12), 3415–3422 (2015)
Jusof, M.F.M., Nasir, A.N.K., Ahmad, M.A., Ibrahim, Z.: An exponential based simulated Kalman filter algorithm for data-driven PID tuning in liquid slosh controller. In: Proceeding of 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 984–987. IEEE, Chiba Japan (2018)
Azwan, A., Razak, A., Jusof, M.F.M., Nasir, A.N.K., Ahmad, M.A.: A multiobjective simulated Kalman filter optimization algorithm. In: Proceeding of 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 23–26. IEEE, Chiba Japan (2018)
Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. In: Zhengzhou University and Nanyang Technological University, Technical Report, Singapore (2013)
Acknowledgements
This work is supported by Fundamental Research Grant Scheme RDU160103 which is awarded by the Ministry of Higher Education Malaysia (MOHE) through Research and Innovation Department and Faculty of Electrical and Electronics Engineering of the Universiti Malaysia Pahang, Malaysia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mat Jusof, M.F. et al. (2019). Simulated Kalman Filter Algorithm with Improved Accuracy. In: Md Zain, Z., et al. Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018 . Lecture Notes in Electrical Engineering, vol 538. Springer, Singapore. https://doi.org/10.1007/978-981-13-3708-6_29
Download citation
DOI: https://doi.org/10.1007/978-981-13-3708-6_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3707-9
Online ISBN: 978-981-13-3708-6
eBook Packages: EngineeringEngineering (R0)