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Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population

Author

Listed:
  • Karim El Moutaouakil

    (Engineering Science Laboratory, FPT, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Abdellatif El Ouissari

    (Engineering Science Laboratory, FPT, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Vasile Palade

    (Centre for Computational Science and Mathematical Modelling, Coventry University, Priory Road, Coventry CV1 5FB, UK)

  • Anas Charroud

    (Engineering Science Laboratory, FPT, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Adrian Olaru

    (Department of Robotics and Production System, University Politehnica of Bucharest, 020771 Bucharest, Romania)

  • Hicham Baïzri

    (MorphoSciences Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech 40001, Morocco)

  • Saliha Chellak

    (Biosciences and Health Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech 40001, Morocco)

  • Mouna Cheggour

    (MorphoSciences Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech 40001, Morocco)

Abstract

To limit the adverse effects of diabetes, a personalized and long-term management strategy that includes appropriate medication, exercise and diet has become of paramount importance and necessity. Compartment-based mathematical control models for diabetes usually result in objective functions whose terms are conflicting, preventing the use of single-objective-based models for obtaining appropriate personalized strategies. Taking into account the conflicting aspects when controlling the diabetic population dynamics, this paper introduces a multi-objective approach consisting of four steps: (a) modeling the problem of controlling the diabetic population dynamics using a multi-objective mathematical model, (b) discretizing the model using the trapezoidal rule and the Euler–Cauchy method, (c) using swarm-intelligence-based optimizers to solve the model and (d) structuring the set of controls using soft clustering methods, known for their flexibility. In contrast to single-objective approaches, experimental results show that the multi-objective approach obtains appropriate personalized controls, where the control associated with the compartment of diabetics without complications is totally different from that associated with the compartment of diabetics with complications. Moreover, these controls enable a significant reduction in the number of diabetics with and without complications, and the multi-objective strategy saves up to 4% of the resources needed for the control of diabetes without complications and up to 18% of resources for the control of diabetes with complications.

Suggested Citation

  • Karim El Moutaouakil & Abdellatif El Ouissari & Vasile Palade & Anas Charroud & Adrian Olaru & Hicham Baïzri & Saliha Chellak & Mouna Cheggour, 2023. "Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population," Mathematics, MDPI, vol. 11(13), pages 1-28, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2957-:d:1185460
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    References listed on IDEAS

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    1. Abdelfatah Kouidere & Omar Balatif & Hanane Ferjouchia & Abdesslam Boutayeb & Mostafa Rachik, 2019. "Optimal Control Strategy for a Discrete Time to the Dynamics of a Population of Diabetics with Highlighting the Impact of Living Environment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-8, December.
    2. H. Föllmer & Y.M. Kabanov, 1997. "Optional decomposition and Lagrange multipliers," Finance and Stochastics, Springer, vol. 2(1), pages 69-81.
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    4. Abdelfatah Kouidere & Abderrahim Labzai & Hanane Ferjouchia & Omar Balatif & Mostafa Rachik, 2020. "A New Mathematical Modeling with Optimal Control Strategy for the Dynamics of Population of Diabetics and Its Complications with Effect of Behavioral Factors," Journal of Applied Mathematics, Hindawi, vol. 2020, pages 1-12, June.
    5. Shoyab Ali & Annapurna Bhargava & Akash Saxena & Pavan Kumar, 2023. "A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
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    Cited by:

    1. Safae Rbihou & Khalid Haddouch & Karim El moutaouakil, 2024. "Optimizing hyperparameters in Hopfield neural networks using evolutionary search," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1245-1273, September.

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