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Detecting Roots of Nonlinear Equations through a Novel Differential Evolution Algorithm

机译:通过一种新的微分进化算法检测非线性方程的根

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Though many numerical methods have been put for nonlinear equations, their convergence and performance are highly sensitive to the initial guesses of the solution pre-supplied.However, the selection of good initial guess is often of hard work.Aiming at this, a novel approach is proposed to resolve nonlinear equations. It takes genetic algorithms' new achievement differential evolution algorithms as the main technique. With a function deflection technique and a novel space contraction method to reinitialize,it resolve nonlinear equations by transform them into correspondent optimization problems. Convergence reliability, computational cost and applicability of different algorithms were compared by testing several classical nonlinear equations and a benchmark mechanics problem. The numerical experiments done show that the put approach has reliable convergence probability,high convergence rate and solution precision. And DE is a successful approach in solving equations both in theory and application.
机译:尽管已经针对非线性方程式采用了许多数值方法,但是它们的收敛性和性能对预先提供的解的初始猜测高度敏感,但是,选择良好的初始猜测往往很困难。提出了求解非线性方程的方法。它以遗传算法的新成果差分进化算法为主要技术。通过函数偏转技术和新颖的空间收缩方法进行重新初始化,通过将非线性方程式转化为相应的优化问题来对其求解。通过测试几个经典的非线性方程和一个基准力学问题,比较了不同算法的收敛可靠性,计算成本和适用性。数值实验表明,该方法具有可靠的收敛概率,较高的收敛速度和求解精度。 DE是在理论上和应用上都能成功求解方程的一种成功方法。

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