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

doi:10.1109/ICCISci.2019.8716382

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 proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	2019 International Conference on Computer and Information Sciences, ICCIS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538681251
DOIs	https://doi.org/10.1109/ICCISci.2019.8716382
Publication status	Published - May 15 2019
Externally published	Yes
Event	2019 International Conference on Computer and Information Sciences, ICCIS 2019 - Sakaka, Saudi Arabia Duration: Apr 3 2019 → Apr 4 2019

Publication series

Name	2019 International Conference on Computer and Information Sciences, ICCIS 2019

Conference

Conference	2019 International Conference on Computer and Information Sciences, ICCIS 2019
Country/Territory	Saudi Arabia
City	Sakaka
Period	4/3/19 → 4/4/19

Keywords

Optimization
Simulated Kalman filter

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1109/ICCISci.2019.8716382

Cite this

Muhammad, B., Ibrahim, Z., Shapiai, M. I., Mohamad, M. S., Azmi, K. Z. M., & Jusof, M. F. M. (2019). Oppositional learning prediction operator with jumping rate for simulated kalman filter. In 2019 International Conference on Computer and Information Sciences, ICCIS 2019 Article 8716382 (2019 International Conference on Computer and Information Sciences, ICCIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCISci.2019.8716382

Oppositional learning prediction operator with jumping rate for simulated kalman filter. / Muhammad, Badaruddin; Ibrahim, Zuwairie; Shapiai, Mohd Ibrahim et al.
2019 International Conference on Computer and Information Sciences, ICCIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8716382 (2019 International Conference on Computer and Information Sciences, ICCIS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Muhammad, B, Ibrahim, Z, Shapiai, MI, Mohamad, MS, Azmi, KZM & Jusof, MFM 2019, Oppositional learning prediction operator with jumping rate for simulated kalman filter. in 2019 International Conference on Computer and Information Sciences, ICCIS 2019., 8716382, 2019 International Conference on Computer and Information Sciences, ICCIS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Conference on Computer and Information Sciences, ICCIS 2019, Sakaka, Saudi Arabia, 4/3/19. https://doi.org/10.1109/ICCISci.2019.8716382

Muhammad B, Ibrahim Z, Shapiai MI, Mohamad MS, Azmi KZM, Jusof MFM. Oppositional learning prediction operator with jumping rate for simulated kalman filter. In 2019 International Conference on Computer and Information Sciences, ICCIS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8716382. (2019 International Conference on Computer and Information Sciences, ICCIS 2019). doi: 10.1109/ICCISci.2019.8716382

Muhammad, Badaruddin ; Ibrahim, Zuwairie ; Shapiai, Mohd Ibrahim et al. / Oppositional learning prediction operator with jumping rate for simulated kalman filter. 2019 International Conference on Computer and Information Sciences, ICCIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 International Conference on Computer and Information Sciences, ICCIS 2019).

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title = "Oppositional learning prediction operator with jumping rate for simulated kalman filter",

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.",

keywords = "Optimization, Simulated Kalman filter",

author = "Badaruddin Muhammad and Zuwairie Ibrahim and Shapiai, {Mohd Ibrahim} and Mohamad, {Mohd Saberi} and Azmi, {Kamil Zakwan Mohd} and Jusof, {Mohd Falfazli Mat}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Computer and Information Sciences, ICCIS 2019 ; Conference date: 03-04-2019 Through 04-04-2019",

year = "2019",

month = may,

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doi = "10.1109/ICCISci.2019.8716382",

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AU - Mohamad, Mohd Saberi

AU - Azmi, Kamil Zakwan Mohd

AU - Jusof, Mohd Falfazli Mat

PY - 2019/5/15

Y1 - 2019/5/15

N2 - 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.

AB - 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.

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BT - 2019 International Conference on Computer and Information Sciences, ICCIS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 3 April 2019 through 4 April 2019

ER -

Oppositional learning prediction operator with jumping rate for simulated kalman filter

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Keywords

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