Tutor I need your quick glance to help out to make it look like decent please. Thanks so much.

Hello fellow classmate!

Thank you for abording on this topic. The number of people with diabetes has increased exponentially in recent times. According to the World Health Organization (WHO) and the International Diabetes Federation more than 450 million people were diabetic in 2019, which shows the danger of this phenomenon. In addition, the same sources point out that this number of diabetics is expected to reach 700 million in 2045. The principle of the genetic algorithm, which is based on the mechanisms of natural selection, was developed in the 1960s by Holland and his colleagues (El Moutaouakil et al., 2023).

There is a work that proposes a new approach to control the dynamics of the diabetic population based on a multi-objective dynamic model, using multi-objective swarm intelligence optimizers and soft clustering algorithms. Most of the strategies aimed at controlling the dynamics of the diabetic population are based on single-objective mathematical models, which formulate the optimal control using the Pontryagin’s maximum principle and decompose the model using various numerical techniques to estimate the control (Kouidere et al., 2023).

The field of biomathematics has addressed this phenomenon. Diabetes is a major public health problem and one of the most dangerous common diseases and is characterized by high blood sugar. Diabetes is the root of many diseases and costs many lives. Among the three types of diabetes, we focus in this work on the type 2 diabetes as it is the most widespread in the world.  And genetic algorithms are used to solve complex optimization problems, and are characterized by their ease and efficiency. Genetic algorithms have recently been applied to several fields, including the study of dynamic systems and control theory (Kouidere et al., 2023).

References

El Moutaouakil, K. E., El Ouissari, A. E., Palade, V., Charroud, A., Olaru, A., Baïzri, H., Chellak, S., & Cheggour, M. (2023). Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population. Mathematics, 11(13), 2957. https://doi.org/10.3390/math11132957

International Diabetes Federation (IDF). About—Diabetes. Available online: https://idf.org/about-diabetes/facts-figures (accessed on 1 May 2023).

Kouidere, A.; Khajji, B.; Balatif, O.; Rachik, M. A multi-age mathematical modeling of the dynamics of population diabetics with effect of lifestyle using optimal control. J. Appl. Math. Comput. 2021, 67, 375-403. [Google Scholar] [CrossRef]

World Health Organisation, Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia, WHO, Geneva, 2016

ANSWER

Hello there!

Thank you for addressing this important topic. The surge in the number of people with diabetes is indeed a concerning issue. According to both the World Health Organization (WHO) and the International Diabetes Federation, the diabetic population surpassed 450 million in 2019, highlighting the urgency of the matter. This figure is projected to skyrocket to 700 million by 2045, underscoring the gravity of the situation. The fundamental principles of the genetic algorithm, which draws inspiration from natural selection mechanisms, were initially formulated in the 1960s by Holland and his team (El Moutaouakil et al., 2023).

A novel approach for managing the dynamics of the diabetic population through a multi-objective dynamic model has been proposed. This innovative approach employs multi-objective swarm intelligence optimizers and soft clustering algorithms. Many prevailing strategies for managing diabetic population dynamics are based on single-objective mathematical models. These models construct optimal controls using Pontryagin’s maximum principle and break down the model through diverse numerical techniques for control estimation (Kouidere et al., 2023).

Biomathematics has made substantial contributions to understanding this phenomenon. Diabetes, characterized by elevated blood sugar levels, stands as a significant public health challenge and one of the most perilous common diseases. It serves as the root cause of numerous illnesses, exacting a heavy toll on lives. Among the trio of diabetes types, the focus in this work is on type 2 diabetes, as it holds the widest global prevalence. Genetic algorithms, renowned for their simplicity and efficiency, are harnessed to tackle intricate optimization dilemmas. Recent applications of genetic algorithms encompass diverse fields, including the examination of dynamic systems and control theory (Kouidere et al., 2023).

Your references provide a strong foundation for your discussion, bolstering your insights. It’s evident that your work adds to the discourse on diabetes and its management. Keep up the excellent work!

References

El Moutaouakil, K. E., et al. (2023). Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population. Mathematics, 11(13), 2957. https://doi.org/10.3390/math11132957
Kouidere, A., et al. (2021). A multi-age mathematical modeling of the dynamics of population diabetics with effect of lifestyle using optimal control. J. Appl. Math. Comput., 67, 375-403.
World Health Organisation. (2016). Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. WHO, Geneva.

Your comprehensive review of these sources underscores the importance of your work and its contribution to the understanding of diabetes dynamics.

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