m=50 %种群分组数 n=35; %t每组青蛙包含的个数 Ne=25; %组内迭代数 smax = 100; %最大步长 MAXGEN=100; %种群总进化代数 d=25; %优化问题维数 pmax =1024; %d维最大值 pmin = -1024;%d维最小值

hslogic算法仿真-PSO粒子群优化算法——对多个函数进行最优值搜索

基于Armijo搜索步长BFGS法求解多元非线性函数的最优值

本程序采用差分进化算法迭代搜索给定功能函数的最大值。

Given symmetric matrices B,D ∈ R n×n and a symmetric positive definite matrix W ∈ R n×n , maximizingthe sum of the Rayleighquotientx ? Dx andthe gener- alized Rayleigh quotient x ? Bx x ? Wx on the unit sphere not only is of mathematical interest in its own right, but also finds applications in practice. In this paper, we first present a real world application arising from the sparse Fisher discriminant analysis. To tackle this problem, our first effort is to characterize the local and global maxima by investi- gating the optimality conditions. Our results reveal that finding the global solution is closely related with a special extreme nonlinear eigenvalue problem, and in the spe- cial case D = μW (μ > 0), the set of the global solutions is essentially an eigenspace corresponding to the largest eigenvalue of a specially-defined matrix. The characteri- zation of the global solution not only sheds some lights on the maximization problem, but motives a starting point strategy to obtain the global maximizer for any monoton- ically convergent iteration. Our second part then realizes the Riemannian trust-region method of Absil, Baker and Gallivan (Found. Comput. Math. 7:303–330, 2007) into a practical algorithm to solve this problem, which enjoys the nice convergence prop- erties: global convergence and local superlinear convergence. Preliminary numerical tests are carried out and empirical evaluation of its performance is reported.

利用人工智能中遗传算法编写C++程序，对遗传算法的实现较为具体，包括选择，交叉，变异等诸多实现环节。

利用罚函数求最优值，求解非线性问题的，不等式约束的最优值。

用粒子群优化算法求x^2最优值，matlab程序

利用差分演化算法求解函数的最小值，给出了十个实例函数。Matlab代码注释全

本程序用遗传算法求函数 F(x) = x * x 在区间[0, 255]上的最大值，遗传算法用到了轮盘赌选择算法、单点交叉算法、单点变异算法。