Simulated Kalman Filter: A novel estimation-based metaheuristic optimization algorithm

Zuwairie Ibrahim, Nor Hidayati Abdul Aziz, Nor Azlina Nor, Saifudin Razali, Mohd Saberi Mohamad

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

In this paper, a novel population-based metaheuristic optimization algorithm, which is named as Simulated Kalman Filter (SKF), is introduced for global optimization problem. This new algorithm is inspired by the estimation capability of the well-known Kalman Filter. In principle, state estimation problem is regarded as an optimization problem and each agent in SKF acts as a Kalman Filter. An agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a bestso- far solution as a reference. To evaluate the performance of the SKF algorithm, it is applied to 30 benchmark functions of CEC 2014 for real-parameter single-objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach and able to outperform some well-known metaheuristic algorithms, such as Genetic Algorithm, Particle Swarm Optimization, Black Hole Algorithm, and Grey Wolf Optimizer.

Original languageEnglish
Pages (from-to)2941-2946
Number of pages6
JournalAdvanced Science Letters
Volume22
Issue number10
DOIs
Publication statusPublished - Oct 2016
Externally publishedYes

Keywords

  • Estimation
  • Kalman
  • Metaheuristics
  • Optimization

ASJC Scopus subject areas

  • General Computer Science
  • Health(social science)
  • General Mathematics
  • Education
  • General Environmental Science
  • General Engineering
  • General Energy

Fingerprint

Dive into the research topics of 'Simulated Kalman Filter: A novel estimation-based metaheuristic optimization algorithm'. Together they form a unique fingerprint.

Cite this