A Population-Based Optimization Method Using Newton Fractal
We propose a deterministic population-based method for a global optimization, a Newton particle optimizer (NPO). The algorithm uses the Newton method with a guiding function and drives particles toward the current best positions. The particles’ movements are influenced by the fractal nature of the N...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/5379301 |
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| _version_ | 1832568340986462208 |
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| author | Soyeong Jeong Pilwon Kim |
| author_facet | Soyeong Jeong Pilwon Kim |
| author_sort | Soyeong Jeong |
| collection | DOAJ |
| description | We propose a deterministic population-based method for a global optimization, a Newton particle optimizer (NPO). The algorithm uses the Newton method with a guiding function and drives particles toward the current best positions. The particles’ movements are influenced by the fractal nature of the Newton method and are greatly diversified in the approach to the temporal best optimums. As a result, NPO generates a wide variety of searching paths, achieving a balance between exploration and exploitation. NPO differs from other metaheuristic methods in that it combines an exact mathematical operation with heuristics and is therefore open to more rigorous analysis. The local and global search of the method can be separately handled as properties of an associated multidimensional mapping. |
| format | Article |
| id | doaj-art-23387f7129ff44429de411bcec52b782 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-23387f7129ff44429de411bcec52b7822025-02-03T00:59:12ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/53793015379301A Population-Based Optimization Method Using Newton FractalSoyeong Jeong0Pilwon Kim1School of Technology Management and Department of Mathematical Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 689-798, Republic of KoreaSchool of Technology Management and Department of Mathematical Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City 689-798, Republic of KoreaWe propose a deterministic population-based method for a global optimization, a Newton particle optimizer (NPO). The algorithm uses the Newton method with a guiding function and drives particles toward the current best positions. The particles’ movements are influenced by the fractal nature of the Newton method and are greatly diversified in the approach to the temporal best optimums. As a result, NPO generates a wide variety of searching paths, achieving a balance between exploration and exploitation. NPO differs from other metaheuristic methods in that it combines an exact mathematical operation with heuristics and is therefore open to more rigorous analysis. The local and global search of the method can be separately handled as properties of an associated multidimensional mapping.http://dx.doi.org/10.1155/2019/5379301 |
| spellingShingle | Soyeong Jeong Pilwon Kim A Population-Based Optimization Method Using Newton Fractal Complexity |
| title | A Population-Based Optimization Method Using Newton Fractal |
| title_full | A Population-Based Optimization Method Using Newton Fractal |
| title_fullStr | A Population-Based Optimization Method Using Newton Fractal |
| title_full_unstemmed | A Population-Based Optimization Method Using Newton Fractal |
| title_short | A Population-Based Optimization Method Using Newton Fractal |
| title_sort | population based optimization method using newton fractal |
| url | http://dx.doi.org/10.1155/2019/5379301 |
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