Single-solution Simulated Kalman Filter algorithm for global optimisation problems

Nor Hidayati Abdul Aziz, Zuwairie Ibrahim, Nor Azlina Ab Aziz, Mohd Saberi Mohamad, Junzo Watada

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Article number103
JournalSadhana - Academy Proceedings in Engineering Sciences
Issue number7
Publication statusPublished - Jul 1 2018
Externally publishedYes


  • adaptive neighbourhood
  • Kalman
  • metaheuristics
  • optimisation
  • Single-solution
  • SKF

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Single-solution Simulated Kalman Filter algorithm for global optimisation problems'. Together they form a unique fingerprint.

Cite this