tent映射初始化种群,并引入随机变量对tent混沌映射改进 matlab代码
时间: 2023-07-02 22:07:18 浏览: 217
以下是一种基于 MATLAB 的实现方法:
```matlab
% 初始化参数
N = 1000; % 种群大小
D = 10; % 解向量维度
xmin = -5.12; % 解向量最小值
xmax = 5.12; % 解向量最大值
a = 1.5; % tent混沌映射参数
x0 = rand(D, N) * (xmax - xmin) + xmin; % 随机初始化种群
% 实现tent混沌映射
x = zeros(D, N);
for i = 1:N
for j = 1:D
if x0(j, i) < 0.5
x(j, i) = a * x0(j, i);
else
x(j, i) = a * (1 - x0(j, i));
end
end
end
% 添加随机变量
sigma = 0.1; % 随机变量标准差
r = randn(D, N) * sigma;
x = x + r;
% 将解向量限制在指定范围内
x = max(x, xmin);
x = min(x, xmax);
```
在上述代码中,我们首先对种群进行随机初始化,然后使用 tent 混沌映射对解向量进行映射。接着,我们添加了随机变量,以增加种群的多样性。最后,我们将解向量限制在指定的范围内,以确保它们符合问题的要求。
相关问题
tent混沌映射初始化种群matlab
Tent混沌映射也可以用来初始化种群。下面是一段MATLAB代码,用Tent混沌映射初始化一个二进制种群:
```
function Pop = tent_init(nPop, nVar, VarMin, VarMax, MaxIt)
% nPop: 种群大小
% nVar: 变量个数
% VarMin: 变量下界
% VarMax: 变量上界
% MaxIt: 迭代次数
% 初始化种群
Pop = zeros(nPop, nVar);
for i = 1:nPop
x = rand(1, nVar);
Pop(i, :) = round(x);
end
% Tent混沌映射参数
mu = 1.9999;
x0 = rand(1, nVar);
% Tent混沌映射初始化
for it = 1:MaxIt
% 产生Tent混沌映射序列
x = mu * x0;
x(x<=0.5) = x(x<=0.5) ./ 0.5;
x(x>0.5) = (1 - x(x>0.5)) ./ 0.5;
x0 = x;
% 更新种群
for i = 1:nPop
for j = 1:nVar
r = rand;
if r < x(j)
Pop(i, j) = 1 - Pop(i, j);
end
end
end
end
% 将种群转换为实数型
Pop = repmat(VarMin, nPop, 1) + Pop.*(repmat(VarMax-VarMin, nPop, 1));
```
其中,`nPop`代表种群大小,`nVar`代表变量个数,`VarMin`和`VarMax`分别代表变量的下界和上界,`MaxIt`代表Tent混沌映射的迭代次数。在初始化过程中,首先随机产生一个二进制种群,然后利用Tent混沌映射对种群进行更新,最后将二进制种群转换成实数型种群。
生成一种基于Tent混沌映射初始化种群的改进粒子群算法的matlab代码
以下是基于Tent混沌映射初始化种群的改进粒子群算法的MATLAB代码:
```matlab
function [gbest, gbestFit, iter] = TentPSO(fitnessFunc, dim, lb, ub, maxIter, popSize, c1, c2, w)
% TentPSO: Improved Particle Swarm Optimization algorithm using Tent map initialization
% Inputs:
% fitnessFunc: function handle for the fitness function
% dim: number of dimensions
% lb: lower bounds of the search space, a vector of length dim
% ub: upper bounds of the search space, a vector of length dim
% maxIter: maximum number of iterations
% popSize: population size
% c1: cognitive parameter
% c2: social parameter
% w: inertia weight
% Outputs:
% gbest: global best solution
% gbestFit: fitness value of global best solution
% iter: number of iterations executed
% Initialize population using Tent map
rng('shuffle');
pop = zeros(popSize, dim);
for i = 1:popSize
pop(i, :) = lb + (ub - lb) * TentMap(rand(), dim);
end
% Initialize velocities
vel = zeros(popSize, dim);
pbest = pop;
pbestFit = zeros(popSize, 1);
for i = 1:popSize
pbestFit(i) = fitnessFunc(pbest(i, :));
end
% Initialize global best
[gbestFit, gbestIdx] = min(pbestFit);
gbest = pbest(gbestIdx, :);
% Run PSO iterations
for iter = 1:maxIter
% Update velocities and positions
vel = w * vel + c1 * rand(popSize, dim) .* (pbest - pop) ...
+ c2 * rand(popSize, dim) .* (gbest - pop);
pop = pop + vel;
% Check for out-of-bounds positions and correct them
for i = 1:popSize
pop(i, :) = max(pop(i, :), lb);
pop(i, :) = min(pop(i, :), ub);
end
% Evaluate fitness and update personal best
for i = 1:popSize
fit = fitnessFunc(pop(i, :));
if fit < pbestFit(i)
pbest(i, :) = pop(i, :);
pbestFit(i) = fit;
end
end
% Update global best
[minFit, minIdx] = min(pbestFit);
if minFit < gbestFit
gbestFit = minFit;
gbest = pbest(minIdx, :);
end
% Update inertia weight
w = w * 0.99;
end
end
function x = TentMap(x0, n)
% TentMap: Generate n Tent map values starting from x0
% Inputs:
% x0: initial value for Tent map
% n: number of values to generate
% Outputs:
% x: a vector of Tent map values
x = zeros(n, 1);
x(1) = x0;
for i = 2:n
if x(i-1) < 0.5
x(i) = 2 * x(i-1);
else
x(i) = 2 - 2 * x(i-1);
end
end
end
```
其中,`TentPSO`函数是主函数,接受上述输入参数并返回最优解、最优解的适应度值和迭代次数。`TentMap`函数用于生成Tent混沌映射序列,用于初始化种群。在`TentPSO`函数中,首先使用Tent混沌映射生成初始种群,然后按照标准粒子群算法的方式更新速度和位置,计算适应度值,更新个体最优解和全局最优解。最后,按照迭代次数递减的方式更新惯性权重。