Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems

Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems

This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisa- tion 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...

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Main Authors: Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Mohd Saberi, Mohamad, Watada, Junzo
Format: Article
Language:English
Published: Indian Academy of Sciences 2016
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/19727/
http://umpir.ump.edu.my/id/eprint/19727/1/Hidayati%20et%20al.%20-%20Single-solution%20simulated%20Kalman%20filter%20algorithm%20for%20global%20optimisation%20problems%20-%20Unknown%20-%20Unknown.pdf
id ump-19727
recordtype eprints
spelling ump-197272018-02-02T03:05:29Z http://umpir.ump.edu.my/id/eprint/19727/ Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Mohd Saberi, Mohamad Watada, Junzo QA75 Electronic computers. Computer science This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisa- tion 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, are produced by random numbers taken from a normal dis- tribution 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 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. Indian Academy of Sciences 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19727/1/Hidayati%20et%20al.%20-%20Single-solution%20simulated%20Kalman%20filter%20algorithm%20for%20global%20optimisation%20problems%20-%20Unknown%20-%20Unknown.pdf Nor Hidayati, Abdul Aziz and Zuwairie, Ibrahim and Nor Azlina, Ab. Aziz and Mohd Saberi, Mohamad and Watada, Junzo (2016) Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems. Sadhana, 123. pp. 2333-2335. ISSN 0973-7677
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Nor Hidayati, Abdul Aziz
Zuwairie, Ibrahim
Nor Azlina, Ab. Aziz
Mohd Saberi, Mohamad
Watada, Junzo
Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
description This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisa- tion 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, are produced by random numbers taken from a normal dis- tribution 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 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.
format Article
author Nor Hidayati, Abdul Aziz
Zuwairie, Ibrahim
Nor Azlina, Ab. Aziz
Mohd Saberi, Mohamad
Watada, Junzo
author_facet Nor Hidayati, Abdul Aziz
Zuwairie, Ibrahim
Nor Azlina, Ab. Aziz
Mohd Saberi, Mohamad
Watada, Junzo
author_sort Nor Hidayati, Abdul Aziz
title Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
title_short Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
title_full Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
title_fullStr Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
title_full_unstemmed Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
title_sort single-solution simulated kalman filter algorithm for global optimisation problems
publisher Indian Academy of Sciences
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/19727/
http://umpir.ump.edu.my/id/eprint/19727/1/Hidayati%20et%20al.%20-%20Single-solution%20simulated%20Kalman%20filter%20algorithm%20for%20global%20optimisation%20problems%20-%20Unknown%20-%20Unknown.pdf
first_indexed 2023-09-18T22:28:16Z
last_indexed 2023-09-18T22:28:16Z
_version_ 1777416112703537152

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