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Single-solution Simulated Kalman Filter algorithm for global optimisation problems

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Abstract

This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisation algorithm inspired by Kalman Filter, for solving real-valued numerical optimisation problems. In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. In the proposed ssSKF algorithm, the initialisation parameters are not constants, but they are produced by random numbers taken from a normal distribution in the range of [0, 1], thus excluding them from tuning requirement. In order to balance between the exploration and exploitation in ssSKF, the proposed algorithm uses an adaptive neighbourhood mechanism during its prediction step. The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to that of the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm and Genetic Algorithm (GA). The results show that the proposed ssSKF algorithm is a promising approach and able to outperform GWO and GA algorithms, significantly.

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Acknowledgements

This research was financially supported by Fundamental Research Grant Scheme (FRGS) awarded by the Ministry of Education (MOE) to Multimedia University under Grant No. FRGS/1/2015/TK04/MMU/03/02 and Universiti Malaysia Pahang under Grant No. RDU140114. We would like to thank Multimedia University and Universiti Malaysia Pahang for providing all the facilities required for this study.

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Authors and Affiliations

  1. Faculty of Engineering and Technology, Multimedia University, 75450, Bukit Beruang, Melaka, Malaysia

    NOR HIDAYATI ABDUL AZIZ & NOR AZLINA AB AZIZ

  2. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    NOR HIDAYATI ABDUL AZIZ

  3. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    ZUWAIRIE IBRAHIM

  4. Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia

    MOHD SABERI MOHAMAD

  5. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Teronoh, Perak, Malaysia

    JUNZO WATADA

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  1. NOR HIDAYATI ABDUL AZIZ
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Correspondence to NOR HIDAYATI ABDUL AZIZ.

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ABDUL AZIZ, N.H., IBRAHIM, Z., AB AZIZ, N.A. et al. Single-solution Simulated Kalman Filter algorithm for global optimisation problems. Sādhanā 43, 103 (2018). https://doi.org/10.1007/s12046-018-0888-9

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  • DOI: https://doi.org/10.1007/s12046-018-0888-9

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