SOLVING NONLINEAR DIFFERENTIAL EQUATIONS USING HYBRID METHOD BETWEEN LYAPUNOV'S ARTIFICIAL SMALL PARAMETER AND CONTINUOUS PARTICLE SWARM OPTIMIZATION

In this paper, Lyapunov's artificial small parameter method (LASP-M) with continuous particle swarm optimization (CPSO) is presented and used for solving nonlinear differential equations. The proposed method, LASPM-CPSO, is based on estimating the epsilon parameter in LASPM through a PSO algorithm and based on a proposed objective function. Three different examples are used to evaluate the proposed method LASPM-CPSO, and compare it with the classical method LASPM through different intervals of the domain. The results from the maximum absolute error (MAE) and mean squared error (MSE) obtained through the given examples show the reliability and efficiency of the proposed LASPM-CPSO method, compared to the classical method LASPM.

Automated summary

This paper presents a method called LASPM-CPSO which uses Lyapunov's artificial small parameter method with continuous particle swarm optimization to solve nonlinear differential equations. The proposed method is evaluated using three different examples and shown to be more reliable and efficient compared to the classical LASPM method.