Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization
Researchers using ordinary differential equations to model phenomena face two main challenges among others: implementing the appropriate model and optimizing the parameters of the selected model. The latter often proves difficult or computationally expensive. Here, we implement Particle Swarm Optimi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2018/9160793 |
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| _version_ | 1832564320820527104 |
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| author | Devin Akman Olcay Akman Elsa Schaefer |
| author_facet | Devin Akman Olcay Akman Elsa Schaefer |
| author_sort | Devin Akman |
| collection | DOAJ |
| description | Researchers using ordinary differential equations to model phenomena face two main challenges among others: implementing the appropriate model and optimizing the parameters of the selected model. The latter often proves difficult or computationally expensive. Here, we implement Particle Swarm Optimization, which draws inspiration from the optimizing behavior of insect swarms in nature, as it is a simple and efficient method for fitting models to data. We demonstrate its efficacy by showing that it outstrips evolutionary computing methods previously used to analyze an epidemic model. |
| format | Article |
| id | doaj-art-41068c77d30e41c2b956509b39384971 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-41068c77d30e41c2b956509b393849712025-02-03T01:11:16ZengWileyJournal of Applied Mathematics1110-757X1687-00422018-01-01201810.1155/2018/91607939160793Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm OptimizationDevin Akman0Olcay Akman1Elsa Schaefer2University of Illinois Urbana-Champaign, USAIllinois State University, USAMarymount University, USAResearchers using ordinary differential equations to model phenomena face two main challenges among others: implementing the appropriate model and optimizing the parameters of the selected model. The latter often proves difficult or computationally expensive. Here, we implement Particle Swarm Optimization, which draws inspiration from the optimizing behavior of insect swarms in nature, as it is a simple and efficient method for fitting models to data. We demonstrate its efficacy by showing that it outstrips evolutionary computing methods previously used to analyze an epidemic model.http://dx.doi.org/10.1155/2018/9160793 |
| spellingShingle | Devin Akman Olcay Akman Elsa Schaefer Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization Journal of Applied Mathematics |
| title | Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization |
| title_full | Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization |
| title_fullStr | Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization |
| title_full_unstemmed | Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization |
| title_short | Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization |
| title_sort | parameter estimation in ordinary differential equations modeling via particle swarm optimization |
| url | http://dx.doi.org/10.1155/2018/9160793 |
| work_keys_str_mv | AT devinakman parameterestimationinordinarydifferentialequationsmodelingviaparticleswarmoptimization AT olcayakman parameterestimationinordinarydifferentialequationsmodelingviaparticleswarmoptimization AT elsaschaefer parameterestimationinordinarydifferentialequationsmodelingviaparticleswarmoptimization |