Single-solution Simulated Kalman Filter algorithm for global optimisation problems


Abdul Aziz, Nor Hidayati and Ibrahim, Zuwairie and Ab Aziz, Nor Azlina and Mohamad, Mohd Saberi and Watada, Junzo (2018) Single-solution Simulated Kalman Filter algorithm for global optimisation problems. Sādhanā, 43 (7). pp. 43-7. ISSN 0256-2499, eISSN: 0973-7677

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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.

Item Type: Article
Uncontrolled Keywords: adaptive,neighbourhood,SKF,Kalman,optimisation,metaheuristics,Single-solution
Subjects: T Technology > TJ Mechanical Engineering and Machinery
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 08 Mar 2021 01:11
Last Modified: 21 Dec 2022 06:12


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