Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm
Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | Ukrainian |
| Published: |
Igor Sikorsky Kyiv Polytechnic Institute
2024-03-01
|
| Series: | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï |
| Subjects: | |
| Online Access: | http://journal.iasa.kpi.ua/article/view/304622 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839649165939834880 |
|---|---|
| author | Абделлатиф Ель Уіссарі Карім Ель Мутауакіль |
| author_facet | Абделлатиф Ель Уіссарі Карім Ель Мутауакіль |
| author_sort | Абделлатиф Ель Уіссарі |
| collection | DOAJ |
| description |
Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth. To capture more detail about this dynamic, non-linear terms were introduced into the mathematical models, resulting in more complicated models strongly consistent with reality (capable of re-producing observable data). The most commonly used methods for control estimation are Pantryagain’s maximum principle and Gumel’s numerical method. However, these methods lead to a costly strategy regarding material and human resources; in addition, diabetologists cannot use the formulas implemented by the proposed controls. In this paper, the authors propose a straightforward and well-performing strategy based on non-linear models and genetic algorithms (GA) that consists of three steps: 1) discretization of the considered non-linear model using classical numerical methods (trapezoidal rule and Euler–Cauchy algorithm); 2) estimation of the optimal control, in several points, based on GA with appropriate fitness function and suitable genetic operators (mutation, crossover, and selection); 3) construction of the optimal control using an interpolation model (splines). The results show that the use of the GA for non-linear models was successfully solved, resulting in a control approach that shows a significant decrease in the number of diabetes cases and diabetics with complications. Remarkably, this result is achieved using less than 70% of available resources.
|
| format | Article |
| id | doaj-art-1ec93702faee4511b7f45c78a8c9f55c |
| institution | Matheson Library |
| issn | 1681-6048 2308-8893 |
| language | Ukrainian |
| publishDate | 2024-03-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï |
| spelling | doaj-art-1ec93702faee4511b7f45c78a8c9f55c2025-06-27T10:30:07ZukrIgor Sikorsky Kyiv Polytechnic InstituteSistemnì Doslìdženâ ta Informacìjnì Tehnologìï1681-60482308-88932024-03-01110.20535/SRIT.2308-8893.2024.1.10343008Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithmАбделлатиф Ель Уіссарі0Карім Ель Мутауакіль1Sidi Mohamed Ben Abdellah University; Higher School of Engineering in Applied Sciences, FesSidi Mohamed Ben Abdellah University, Fez Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth. To capture more detail about this dynamic, non-linear terms were introduced into the mathematical models, resulting in more complicated models strongly consistent with reality (capable of re-producing observable data). The most commonly used methods for control estimation are Pantryagain’s maximum principle and Gumel’s numerical method. However, these methods lead to a costly strategy regarding material and human resources; in addition, diabetologists cannot use the formulas implemented by the proposed controls. In this paper, the authors propose a straightforward and well-performing strategy based on non-linear models and genetic algorithms (GA) that consists of three steps: 1) discretization of the considered non-linear model using classical numerical methods (trapezoidal rule and Euler–Cauchy algorithm); 2) estimation of the optimal control, in several points, based on GA with appropriate fitness function and suitable genetic operators (mutation, crossover, and selection); 3) construction of the optimal control using an interpolation model (splines). The results show that the use of the GA for non-linear models was successfully solved, resulting in a control approach that shows a significant decrease in the number of diabetes cases and diabetics with complications. Remarkably, this result is achieved using less than 70% of available resources. http://journal.iasa.kpi.ua/article/view/304622optimal controldifferential equationdiabetesgenetic algorithmsartificial intelligenceintelligent local search |
| spellingShingle | Абделлатиф Ель Уіссарі Карім Ель Мутауакіль Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï optimal control differential equation diabetes genetic algorithms artificial intelligence intelligent local search |
| title | Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm |
| title_full | Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm |
| title_fullStr | Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm |
| title_full_unstemmed | Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm |
| title_short | Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm |
| title_sort | intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm |
| topic | optimal control differential equation diabetes genetic algorithms artificial intelligence intelligent local search |
| url | http://journal.iasa.kpi.ua/article/view/304622 |
| work_keys_str_mv | AT abdellatifelʹuíssarí intelligentoptimalcontrolofnonlineardiabeticpopulationdynamicssystemusingageneticalgorithm AT karímelʹmutauakílʹ intelligentoptimalcontrolofnonlineardiabeticpopulationdynamicssystemusingageneticalgorithm |