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A Comparison of Particle Swarm optimization and Global African Buffalo Optimization

The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of a...

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Main Authors: Adam Kunna Azrag, Mohammed, Tuty Asmawaty, Abdul Kadir, Noorlin, Mohd Ali
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing 2020
Subjects:
QA Mathematics
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/28804/1/A%20comparison%20of%20particle%20swarm%20optimization%20and%20global.pdf
http://umpir.ump.edu.my/id/eprint/28804/
https://doi.org/10.1088/1757-899X/769/1/012034
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spelling my.ump.umpir.288042022-12-13T04:24:25Z http://umpir.ump.edu.my/id/eprint/28804/ A Comparison of Particle Swarm optimization and Global African Buffalo Optimization Adam Kunna Azrag, Mohammed Tuty Asmawaty, Abdul Kadir Noorlin, Mohd Ali QA Mathematics QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of algorithms. In addition, the late proposed algorithms mostly are compared to the well-known algorithm such as PSO. Thus, the Global African Buffalo Optimization (GABO) was proposed lately and yet not been compared to the old well-known algorithms in terms of accuracy and time consumption. However, in this paper, a comparison between Particle Swarm Optimization (PSO) and Global African Buffalo Optimization (GABO) algorithms was performed. Five different nonlinear equations with their upper and lower boundaries values were selected as the test optimization functions problem in addition to PSO was applied to real case study. The experimental results illustrated the differences in the performances of both algorithms toward the optimum solution. At the end of the experiments, the PSO algorithm quickly convergence towards the optimum solution using a few particles and iterations rather than GABO. However, the experimental result showed that PSO achieved good results in all the test cases within a short time. In many cases, PSO and GABO are promising optimization methods. IOP Publishing 2020-06-05 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/28804/1/A%20comparison%20of%20particle%20swarm%20optimization%20and%20global.pdf Adam Kunna Azrag, Mohammed and Tuty Asmawaty, Abdul Kadir and Noorlin, Mohd Ali (2020) A Comparison of Particle Swarm optimization and Global African Buffalo Optimization. In: IOP Conference Series: Materials Science and Engineering, 6th International Conference on Software Engineering and Computer Systems, ICSECS 2019, 25 - 27 September 2019 , Vistana Hotel, Kuantan. pp. 1-12., 769 (012034). ISSN 1757-8981 (Print), 1757-899X (Online) https://doi.org/10.1088/1757-899X/769/1/012034
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA Mathematics
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Adam Kunna Azrag, Mohammed
Tuty Asmawaty, Abdul Kadir
Noorlin, Mohd Ali
A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
description The performance of Particle Swarm Optimization (PSO) brings attention to the field of algorithms when deals with different optimization problems. Due to her simple implementation, small consumption, and very effective in finding a solution in many problems, (PSO) becomes well known to the field of algorithms. In addition, the late proposed algorithms mostly are compared to the well-known algorithm such as PSO. Thus, the Global African Buffalo Optimization (GABO) was proposed lately and yet not been compared to the old well-known algorithms in terms of accuracy and time consumption. However, in this paper, a comparison between Particle Swarm Optimization (PSO) and Global African Buffalo Optimization (GABO) algorithms was performed. Five different nonlinear equations with their upper and lower boundaries values were selected as the test optimization functions problem in addition to PSO was applied to real case study. The experimental results illustrated the differences in the performances of both algorithms toward the optimum solution. At the end of the experiments, the PSO algorithm quickly convergence towards the optimum solution using a few particles and iterations rather than GABO. However, the experimental result showed that PSO achieved good results in all the test cases within a short time. In many cases, PSO and GABO are promising optimization methods.
format Conference or Workshop Item
author Adam Kunna Azrag, Mohammed
Tuty Asmawaty, Abdul Kadir
Noorlin, Mohd Ali
author_facet Adam Kunna Azrag, Mohammed
Tuty Asmawaty, Abdul Kadir
Noorlin, Mohd Ali
author_sort Adam Kunna Azrag, Mohammed
title A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
title_short A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
title_full A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
title_fullStr A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
title_full_unstemmed A Comparison of Particle Swarm optimization and Global African Buffalo Optimization
title_sort comparison of particle swarm optimization and global african buffalo optimization
publisher IOP Publishing
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/28804/1/A%20comparison%20of%20particle%20swarm%20optimization%20and%20global.pdf
http://umpir.ump.edu.my/id/eprint/28804/
https://doi.org/10.1088/1757-899X/769/1/012034
_version_ 1752146610207326208
score 13.211869

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