Midrange exploration exploitation searching particle swarm optimization in dynamic environment
Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32190/ http://umpir.ump.edu.my/id/eprint/32190/3/Midrange%20exploration%20exploitation1.pdf http://umpir.ump.edu.my/id/eprint/32190/4/Midrange%20exploration%20exploitation%20searching.pdf |
| _version_ | 1848823957672689664 |
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| author | Nurul Izzatie Husna, Fauzi Zalili, Musa Nor Saradatul Akmar, Zulkifli |
| author_facet | Nurul Izzatie Husna, Fauzi Zalili, Musa Nor Saradatul Akmar, Zulkifli |
| author_sort | Nurul Izzatie Husna, Fauzi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we proposed a new enhancement method of conventional Particle Swarm Optimization called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO). The main objective of this improvement is to enhance the searching ability of poor particles in finding the best solution in dynamic problems. In MEESPSO, we still applied the basic process in conventional Particle Swarm Optimization such as initialization of particle location, population evolution, and updating particle location. However, we added some enhancement processes in MEESPSO such as updating the location of new poor particles based on the average value of the particle minimum fitness and maximum fitness. To see the performance of the proposed method, we compare the proposed method with three existing methods such as Conventional Particle Swarm Optimization, Differential Evaluation Particle Swarm Optimization, and Global Best Local Neighborhood Particle Swarm Optimization. Based on the experimental result of 50 datasets show that MEESPSO can find the quality solution in term of number of particle and iteration, consistency, convergence, optimum value, and error rate. |
| first_indexed | 2025-11-15T03:05:23Z |
| format | Conference or Workshop Item |
| id | ump-32190 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:05:23Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-321902022-06-10T07:10:46Z http://umpir.ump.edu.my/id/eprint/32190/ Midrange exploration exploitation searching particle swarm optimization in dynamic environment Nurul Izzatie Husna, Fauzi Zalili, Musa Nor Saradatul Akmar, Zulkifli QA76 Computer software Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we proposed a new enhancement method of conventional Particle Swarm Optimization called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO). The main objective of this improvement is to enhance the searching ability of poor particles in finding the best solution in dynamic problems. In MEESPSO, we still applied the basic process in conventional Particle Swarm Optimization such as initialization of particle location, population evolution, and updating particle location. However, we added some enhancement processes in MEESPSO such as updating the location of new poor particles based on the average value of the particle minimum fitness and maximum fitness. To see the performance of the proposed method, we compare the proposed method with three existing methods such as Conventional Particle Swarm Optimization, Differential Evaluation Particle Swarm Optimization, and Global Best Local Neighborhood Particle Swarm Optimization. Based on the experimental result of 50 datasets show that MEESPSO can find the quality solution in term of number of particle and iteration, consistency, convergence, optimum value, and error rate. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32190/3/Midrange%20exploration%20exploitation1.pdf pdf en http://umpir.ump.edu.my/id/eprint/32190/4/Midrange%20exploration%20exploitation%20searching.pdf Nurul Izzatie Husna, Fauzi and Zalili, Musa and Nor Saradatul Akmar, Zulkifli (2021) Midrange exploration exploitation searching particle swarm optimization in dynamic environment. In: 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) , 21-26 August 2021 , Pekan, Pahang, Malaysia. pp. 649-654.. ISBN 978-1-6654-1407-4 (Published) https://doi.org/10.1109/ICSECS52883.2021.00124 http://10.1109/ICSECS52883.2021.00124 |
| spellingShingle | QA76 Computer software Nurul Izzatie Husna, Fauzi Zalili, Musa Nor Saradatul Akmar, Zulkifli Midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| title | Midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| title_full | Midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| title_fullStr | Midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| title_full_unstemmed | Midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| title_short | Midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| title_sort | midrange exploration exploitation searching particle swarm optimization in dynamic environment |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/32190/ http://umpir.ump.edu.my/id/eprint/32190/ http://umpir.ump.edu.my/id/eprint/32190/ http://umpir.ump.edu.my/id/eprint/32190/3/Midrange%20exploration%20exploitation1.pdf http://umpir.ump.edu.my/id/eprint/32190/4/Midrange%20exploration%20exploitation%20searching.pdf |