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Inertia weight strategies in GbLN-PSO for optimum solution

Particle Swarm Optimization (PSO) is the popular metaheuristic search algorithm that is inspired by the social learning of birds and fish. In the PSO algorithm, inertia weight is an important parameter to determine the searching ability of each particle. When the selected inertia weight is not suita...

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Main Authors: Nurul Izzatie Husna, Fauzi, Zalili, Musa
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40373/1/Inertia%20weight%20strategies%20in%20GbLN-PSO%20for%20optimum%20solution.pdf
http://umpir.ump.edu.my/id/eprint/40373/2/Inertia%20weight%20strategies%20in%20GbLN-PSO%20for%20optimum%20solution_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40373/
https://doi.org/10.1109/ICSECS58457.2023.10256385
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spelling my.ump.umpir.403732024-04-16T04:17:50Z http://umpir.ump.edu.my/id/eprint/40373/ Inertia weight strategies in GbLN-PSO for optimum solution Nurul Izzatie Husna, Fauzi Zalili, Musa QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Particle Swarm Optimization (PSO) is the popular metaheuristic search algorithm that is inspired by the social learning of birds and fish. In the PSO algorithm, inertia weight is an important parameter to determine the searching ability of each particle. When the selected inertia weight is not suitable, the searching particles are more focused on one direction or area nearest to the local best. Therefore, the movement of the particles is limited and not spreading during the search process. Thus, this will cause the particles fast to converge. As the result, the particle is trapped in local optimal. To overcome this problem, we used three different inertia weight strategies such as Constant Inertia Weight (CIW), Random Inertia Weight (RIW), and Linear Decreasing Inertia Weight (LDIW) to analyze the impact of inertia weight on the performance of Conventional PSO and the enhancement of PSO called Global Best Local Neighborhood-PSO (GbLN-PSO) algorithm. In order to test the performance of the three different inertia weight strategies, we test these algorithms in different sizes of search space with random values. Based on the comparison result of 30 simulations, it shows that GbLN-PSO using RIW was producing a better search result compared to CIW and LDIW. Furthermore, the result shows an improvement in GbLN-PSO searching ability. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40373/1/Inertia%20weight%20strategies%20in%20GbLN-PSO%20for%20optimum%20solution.pdf pdf en http://umpir.ump.edu.my/id/eprint/40373/2/Inertia%20weight%20strategies%20in%20GbLN-PSO%20for%20optimum%20solution_ABS.pdf Nurul Izzatie Husna, Fauzi and Zalili, Musa (2023) Inertia weight strategies in GbLN-PSO for optimum solution. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 424-429. (192961). ISBN 979-835031093-1 https://doi.org/10.1109/ICSECS58457.2023.10256385
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Nurul Izzatie Husna, Fauzi
Zalili, Musa
Inertia weight strategies in GbLN-PSO for optimum solution
description Particle Swarm Optimization (PSO) is the popular metaheuristic search algorithm that is inspired by the social learning of birds and fish. In the PSO algorithm, inertia weight is an important parameter to determine the searching ability of each particle. When the selected inertia weight is not suitable, the searching particles are more focused on one direction or area nearest to the local best. Therefore, the movement of the particles is limited and not spreading during the search process. Thus, this will cause the particles fast to converge. As the result, the particle is trapped in local optimal. To overcome this problem, we used three different inertia weight strategies such as Constant Inertia Weight (CIW), Random Inertia Weight (RIW), and Linear Decreasing Inertia Weight (LDIW) to analyze the impact of inertia weight on the performance of Conventional PSO and the enhancement of PSO called Global Best Local Neighborhood-PSO (GbLN-PSO) algorithm. In order to test the performance of the three different inertia weight strategies, we test these algorithms in different sizes of search space with random values. Based on the comparison result of 30 simulations, it shows that GbLN-PSO using RIW was producing a better search result compared to CIW and LDIW. Furthermore, the result shows an improvement in GbLN-PSO searching ability.
format Conference or Workshop Item
author Nurul Izzatie Husna, Fauzi
Zalili, Musa
author_facet Nurul Izzatie Husna, Fauzi
Zalili, Musa
author_sort Nurul Izzatie Husna, Fauzi
title Inertia weight strategies in GbLN-PSO for optimum solution
title_short Inertia weight strategies in GbLN-PSO for optimum solution
title_full Inertia weight strategies in GbLN-PSO for optimum solution
title_fullStr Inertia weight strategies in GbLN-PSO for optimum solution
title_full_unstemmed Inertia weight strategies in GbLN-PSO for optimum solution
title_sort inertia weight strategies in gbln-pso for optimum solution
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40373/1/Inertia%20weight%20strategies%20in%20GbLN-PSO%20for%20optimum%20solution.pdf
http://umpir.ump.edu.my/id/eprint/40373/2/Inertia%20weight%20strategies%20in%20GbLN-PSO%20for%20optimum%20solution_ABS.pdf
http://umpir.ump.edu.my/id/eprint/40373/
https://doi.org/10.1109/ICSECS58457.2023.10256385
_version_ 1822924225298563072
score 13.232414

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