Riemannian流形中DE算法算子最优特征量的量子渐进估计

Quantum Asymptotic Estimation of the Optimal Eigenvalues of DE Operators in Riemannian Manifolds

该文主要分析和探讨了差分进化算法(Differential Eveolutionary Algorithm,DE)在Riemannian流形中的几何关系,对P-ε条件下Riemannian流形中的种群个体进行了收敛性分析,得到了迭代个体收敛精度与收敛速度的量子不确定渐进估计,如下式Δv^2 Δxβ^ε^2≥(√(λε)1+…+√(λε)n/2)^2,其中,Δv^2为种群个体的速度分辨率,Δxβ^ε^2为种群个体带有误差的位置分辨率,(λε)i,i=1,2,…,n.从本质上说明了Riemannian流形中迭代个体的局部特征量是不能从收敛精度和收敛速度同时达到算法高效.

In this paper,the geometric relations of differential evolution algorithm in Riemannian manifolds are analyzed and discussed.The convergence of populations in Riemannian manifolds with P-εis analyzed.A quantum uncertain asymptotic estimation of the convergence accuracy and convergence speed of the iterative individual is obtained as follows Δv^2 Δxβ^ε^2≥(√(λε)1+…+√(λε)n/2)^2,where,Δv^2 is speed resolution of individual populations,Δxβ^ε^2 is position resolution with errorεof individual populations,(λε)i,i=1,2,…,n.The theorem expression essentially shows that the local eigenvalues of iterated individuals in Riemann manifolds can not achieve high convergent accuracy and convergent speed at the same time.