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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| 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 |
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| author | Nurul Izzatie Husna, Fauzi Zalili, Musa |
| author_facet | Nurul Izzatie Husna, Fauzi Zalili, Musa |
| author_sort | Nurul Izzatie Husna, Fauzi |
| collection | UMP |
| 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. |
| first_indexed | 2024-04-22T01:26:02Z |
| format | Conference or Workshop Item |
| id | UMPir40373 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-04-22T01:26:02Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir403732024-04-16T04:17:50Z 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 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256385 |
| 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 |
| title | 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_short | Inertia weight strategies in GbLN-PSO for optimum solution |
| title_sort | inertia weight strategies in gbln pso for optimum solution |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| 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 |
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