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Detecting Roots of Nonlinear Equations through a Novel Differential Evolution Algorithm

机译:一种通过新型差分演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:虽然许多数值方法已经为非线性方程进行了,但它们的收敛性和性能对溶液预先提供的解决方案的初始猜测非常敏感。但是,良好的初步猜测的选择通常是艰苦的工作。这是一个新的方法建议解决非线性方程。它需要遗传算法的新成就差分演进算法作为主要技术。通过功能偏转技术和重新初始化的新型空间收缩方法,它通过将它们转换为对应的优化问题来解析非线性方程。通过测试几种经典非线性方程和基准力学问题,比较了不同算法的收敛可靠性,计算成本和适用性。完成的数值实验表明,PUT方法具有可靠的收敛概率,高收敛速率和溶液精度。并且de是在理论和应用中解决方程的成功方法。

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