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Improved opposition-based particle swarm optimization algorithm for global optimization

Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of in...

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Main Authors: Nafees Ul Hassan, Waqas Haider Bangyal, M. Sadiq Ali Khan, Kashif Nisar, Ag. Asri Ag. Ibrahim, Danda B. Rawat
Format: Article
Language:English
English
Published: MDPI AG 2021
Subjects:
QC1-75 General
Online Access:https://eprints.ums.edu.my/id/eprint/31852/1/Improved%20opposition-based%20particle%20swarm%20optimization%20algorithm%20for%20global%20optimization.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/31852/2/Improved%20opposition-based%20particle%20swarm%20optimization%20algorithm%20for%20global%20optimization.pdf
https://eprints.ums.edu.my/id/eprint/31852/
https://www.mdpi.com/2073-8994/13/12/2280
https://doi.org/10.3390/sym13122280
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spelling my.ums.eprints.318522022-03-15T03:45:14Z https://eprints.ums.edu.my/id/eprint/31852/ Improved opposition-based particle swarm optimization algorithm for global optimization Nafees Ul Hassan Waqas Haider Bangyal M. Sadiq Ali Khan Kashif Nisar Ag. Asri Ag. Ibrahim Danda B. Rawat QC1-75 General Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence. MDPI AG 2021-12-10 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/31852/1/Improved%20opposition-based%20particle%20swarm%20optimization%20algorithm%20for%20global%20optimization.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/31852/2/Improved%20opposition-based%20particle%20swarm%20optimization%20algorithm%20for%20global%20optimization.pdf Nafees Ul Hassan and Waqas Haider Bangyal and M. Sadiq Ali Khan and Kashif Nisar and Ag. Asri Ag. Ibrahim and Danda B. Rawat (2021) Improved opposition-based particle swarm optimization algorithm for global optimization. Symmetry, 13. pp. 1-23. ISSN 2073-8994 https://www.mdpi.com/2073-8994/13/12/2280 https://doi.org/10.3390/sym13122280
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QC1-75 General
spellingShingle QC1-75 General
Nafees Ul Hassan
Waqas Haider Bangyal
M. Sadiq Ali Khan
Kashif Nisar
Ag. Asri Ag. Ibrahim
Danda B. Rawat
Improved opposition-based particle swarm optimization algorithm for global optimization
description Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence.
format Article
author Nafees Ul Hassan
Waqas Haider Bangyal
M. Sadiq Ali Khan
Kashif Nisar
Ag. Asri Ag. Ibrahim
Danda B. Rawat
author_facet Nafees Ul Hassan
Waqas Haider Bangyal
M. Sadiq Ali Khan
Kashif Nisar
Ag. Asri Ag. Ibrahim
Danda B. Rawat
author_sort Nafees Ul Hassan
title Improved opposition-based particle swarm optimization algorithm for global optimization
title_short Improved opposition-based particle swarm optimization algorithm for global optimization
title_full Improved opposition-based particle swarm optimization algorithm for global optimization
title_fullStr Improved opposition-based particle swarm optimization algorithm for global optimization
title_full_unstemmed Improved opposition-based particle swarm optimization algorithm for global optimization
title_sort improved opposition-based particle swarm optimization algorithm for global optimization
publisher MDPI AG
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/31852/1/Improved%20opposition-based%20particle%20swarm%20optimization%20algorithm%20for%20global%20optimization.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/31852/2/Improved%20opposition-based%20particle%20swarm%20optimization%20algorithm%20for%20global%20optimization.pdf
https://eprints.ums.edu.my/id/eprint/31852/
https://www.mdpi.com/2073-8994/13/12/2280
https://doi.org/10.3390/sym13122280
_version_ 1760230947332030464
score 13.211869

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