A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution
The conventional Particle Swarm Optimization (PSO) was introduced as an optimization technique for real applications such as image processing, tracking, localization, and scheduling. However, conventional PSO still has its limitation in finding optimal solutions and is always trapped in the local op...
| Κύριοι συγγραφείς: | , |
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| Μορφή: | Conference or Workshop Item |
| Γλώσσα: | English English |
| Έκδοση: |
Institute of Electrical and Electronics Engineers Inc.
2023
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| Διαθέσιμο Online: | http://umpir.ump.edu.my/id/eprint/40315/1/A%20new%20approach%20of%20midrange%20exploration%20exploitation.pdf http://umpir.ump.edu.my/id/eprint/40315/2/A%20new%20approach%20of%20midrange%20exploration%20exploitation%20searching%20particle%20swarm%20%20optimization%20for%20optimal%20solution_ABS.pdf |
| _version_ | 1825815462503514112 |
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| author | Nurul Izzatie Husna, Muhamad Fauzi Zalili, Musa |
| author_facet | Nurul Izzatie Husna, Muhamad Fauzi Zalili, Musa |
| author_sort | Nurul Izzatie Husna, Muhamad Fauzi |
| collection | UMP |
| description | The conventional Particle Swarm Optimization (PSO) was introduced as an optimization technique for real applications such as image processing, tracking, localization, and scheduling. However, conventional PSO still has its limitation in finding optimal solutions and is always trapped in the local optima. Therefore, the concept of conventional PSO was unsuitable to be used in dynamic problems. In order to address these issues, we have introduced a novel enhancement approach known as Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) to categorize the particle into resident particles and migrant particles according to midrange value. A migrant particle will execute the process of exploration to other search spaces, meanwhile resident particles went through the process of exploitation accordingly to the best solution. The comparison result shows that MEESPSO has the talent to increase the accuracy in a real application. |
| first_indexed | 2024-04-22T01:25:47Z |
| format | Conference or Workshop Item |
| id | UMPir40315 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-04-22T01:25:47Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir403152024-04-16T04:05:20Z http://umpir.ump.edu.my/id/eprint/40315/ A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution Nurul Izzatie Husna, Muhamad Fauzi Zalili, Musa QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) The conventional Particle Swarm Optimization (PSO) was introduced as an optimization technique for real applications such as image processing, tracking, localization, and scheduling. However, conventional PSO still has its limitation in finding optimal solutions and is always trapped in the local optima. Therefore, the concept of conventional PSO was unsuitable to be used in dynamic problems. In order to address these issues, we have introduced a novel enhancement approach known as Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) to categorize the particle into resident particles and migrant particles according to midrange value. A migrant particle will execute the process of exploration to other search spaces, meanwhile resident particles went through the process of exploitation accordingly to the best solution. The comparison result shows that MEESPSO has the talent to increase the accuracy in a real application. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40315/1/A%20new%20approach%20of%20midrange%20exploration%20exploitation.pdf pdf en http://umpir.ump.edu.my/id/eprint/40315/2/A%20new%20approach%20of%20midrange%20exploration%20exploitation%20searching%20particle%20swarm%20%20optimization%20for%20optimal%20solution_ABS.pdf Nurul Izzatie Husna, Muhamad Fauzi and Zalili, Musa (2023) A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 430-434. (192961). ISBN 979-835031093-1 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256375 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Nurul Izzatie Husna, Muhamad Fauzi Zalili, Musa A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution |
| title | A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution |
| title_full | A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution |
| title_fullStr | A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution |
| title_full_unstemmed | A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution |
| title_short | A new approach of midrange exploration exploitation searching particle swarm optimization for optimal solution |
| title_sort | new approach of midrange exploration exploitation searching particle swarm optimization for optimal 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/40315/1/A%20new%20approach%20of%20midrange%20exploration%20exploitation.pdf http://umpir.ump.edu.my/id/eprint/40315/2/A%20new%20approach%20of%20midrange%20exploration%20exploitation%20searching%20particle%20swarm%20%20optimization%20for%20optimal%20solution_ABS.pdf |
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