Single-solution Simulated Kalman Filter algorithm for global optimisation problems

Abdul Aziz, N.H. and Ibrahim, Z. and Ab Aziz, N.A. and Mohamad, M.S. and Watada, J. (2018) Single-solution Simulated Kalman Filter algorithm for global optimisation problems. Sadhana - Academy Proceedings in Engineering Sciences, 43 (7).

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


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. © 2018, Indian Academy of Sciences.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Bandpass filters; Benchmarking; Genetic algorithms; Global optimization; Normal distribution; Parameter estimation; Particle swarm optimization (PSO); Software agents, Exploration and exploitation; Kalman; Kalman filter algorithms; Meta heuristics; Neighbourhood; Optimisation problems; Optimisations; Particle swarm optimisation, Kalman filters
ID Code:20827
Deposited By: Ahmad Suhairi
Deposited On:26 Feb 2019 02:26
Last Modified:26 Feb 2019 02:26

Repository Staff Only: item control page