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Numerical treatment of nonlinear model of virus propagation in computer networks: an innovative evolutionary Pad?? approximation scheme

机译:计算机网络中病毒传播的非线性模型的数值处理:一种创新的进化Pad?近似方案

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摘要

This work proposes a novel mesh free evolutionary Pad?? approximation (EPA) framework for obtaining closed form numerical solution of a nonlinear dynamical continuous model of virus propagation in computer networks. The proposed computational architecture of EPA scheme assimilates a Pad?? approximation to transform the underlying nonlinear model to an equivalent optimization problem. Initial conditions, dynamical positivity and boundedness are dealt with as problem constraints and are handled through penalty function approach. Differential evolution is employed to obtain closed form numerical solution of the model by solving the developed optimization problem. The numerical results of EPA are compared with finite difference schemes like fourth order Rungea??Kutta (RK-4), ODE45 and Euler methods. Contrary to these standard methods, the proposed EPA scheme is independent of the choice of step lengths and unconditionally converges to true steady state points. An error analysis based on residuals witnesses that the convergence speed of EPA is higher than a globally convergent non-standard finite difference (NSFD) scheme for smaller as well as larger time steps.
机译:这项工作提出了一种新颖的无网格进化垫?近似(EPA)框架,用于获得计算机网络中病毒传播的非线性动态连续模型的闭合形式数值解。 EPA方案的拟议计算体系结构吸收了Pad?近似将基础非线性模型转换为等效的优化问题。初始条件,动力积极性和有界性被视为问题约束,并通过惩罚函数方法进行处理。通过解决发展中的优化问题,采用微分进化来获得模型的封闭形式数值解。将EPA的数值结果与四阶Rungea ?? Kutta(RK-4),ODE45和Euler方法等有限差分方案进行比较。与这些标准方法相反,建议的EPA方案与步长的选择无关,并且无条件地收敛到真正的稳态点。基于残差的误差分析表明,对于较小的时间步长和较大的时间步长,EPA的收敛速度都高于全局收敛的非标准有限差分(NSFD)方案。

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