Delay Differential Equations (DDEs) are differential equations with time
delays, which are used in various life sciences disciplines like
population dynamics, epidemiology, immunology and physiology. These
models are used to analyze and predict phenomena, such as the duration
of latent processes like the life cycle, infection, and immune response.
The dynamics of a system at a given moment in time are influenced by its
previous history or memory, increasing the complexity of the system and
improving the dynamics of a differential model. Fractional models of
DDEs have been used to explore various phenomena, such as brain
networking, population dynamics, and physiology. This study investigates
the numerical approximations of the Nonlinear Fractional Pantograph
Delay Differential Equation (NFPDDE) using the Fractional Novel
Analytical Scheme (FNAS) and optimization procedures based on the
Genetic Algorithm (GA), referred as Fractional Novel Analytical Genetic
Algorithm (FNAGA). The FNAGA is used to optimize an error-based fitness
function constructed through fractional delay differential equations.
The Conformable fractional derivative T η is taken into consideration.
To implement the proposed methodology, an error analysis is conducted.
The solution behavior of NFPDDEs is also shown graphically at different
values of η. The findings of the FNAGA are contrasted with those
of the FNAS, indicating that the newly developed algorithm exhibits
rapid convergence, produces precise solutions, and demonstrates enhanced
accuracy. The effectiveness of the proposed method in achieving the
synchronization objective is demonstrated through simulations and can be
easily applied to various fractional models.