Oppositional learning prediction operator with jumping rate for simulated kalman filter

Badaruddin Muhammad, Zuwairie Ibrahim, Mohd Ibrahim Shapiai, Mohd Saberi Mohamad, Kamil Zakwan Mohd Azmi, Mohd Falfazli Mat Jusof

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning with a jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases.

Original languageEnglish
Title of host publication2019 International Conference on Computer and Information Sciences, ICCIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538681251
DOIs
Publication statusPublished - May 15 2019
Externally publishedYes
Event2019 International Conference on Computer and Information Sciences, ICCIS 2019 - Sakaka, Saudi Arabia
Duration: Apr 3 2019Apr 4 2019

Publication series

Name2019 International Conference on Computer and Information Sciences, ICCIS 2019

Conference

Conference2019 International Conference on Computer and Information Sciences, ICCIS 2019
Country/TerritorySaudi Arabia
CitySakaka
Period4/3/194/4/19

Keywords

  • Optimization
  • Simulated Kalman filter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management

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