V(j,find(V(j,:)>Vmax))V(j,find(V(j,:)>Vmax))
时间: 2024-04-11 20:32:56 浏览: 6
`V(j,find(V(j,:)>Vmax))` 是一种MATLAB语法,它表示从一个名为 `V` 的矩阵中选择第 `j` 行中大于 `Vmax` 的元素。`find(V(j,:)>Vmax)` 是一个逻辑索引操作,它返回一个逻辑向量,其中为 `true` 的元素对应于满足条件的元素的索引。然后,这个逻辑索引被用于选择 `V(j,:)` 中相应的元素。
相关问题
clear f = @(x,y) 20 + x.^2 + y.^2 - 10*cos(2*pi.*x) - 10*cos(2*pi.*y) ; x0 = [-5.12:0.05:5.12]; y0 = x0 ; [X,Y] = meshgrid(x0,y0); Z =f(X,Y) ; figure(1); mesh(X,Y,Z); colormap(parula(5)); n = 10; narvs = 2; c1 = 0.6; c2 = 0.6; w_max = 0.9; w_min = 0.4; K = 100; vmax = 1.2; x_lb = -5.12; x_ub = 5.12; x = zeros(n,narvs); x = x_lb + (x_ub-x_lb).*rand(n,narvs) v = -vmax + 2*vmax .* rand(n,narvs); fit = zeros(n,1); for i = 1:n fit(i) = Obj_fun1(x(i,:)); end pbest = x; ind = find(fit == max(fit), 1); gbest = x(ind,:); h = scatter(x,fit,80,'*r'); fitnessbest = ones(K,1); for d = 1:K for i = 1:n f_i = fit(i); f_avg = sum(fit)/n; f_max = max(fit); if f_i >= f_avg if f_avg ~= f_max w = w_min + (w_max - w_min)*(f_max - f_i)/(f_max - f_avg); else w = w_max; end else w = w_max; end v(i,:) = w*v(i,:) + c1*rand(1)*(pbest(i,:) - x(i,:)) + c2*rand(1)*(gbest - x(i,:)); for j = 1: narvs if v(i,j) < -vmax(j) v(i,j) = -vmax(j); elseif v(i,j) > vmax(j) v(i,j) = vmax(j); end end x(i,:) = x(i,:) + v(i,:); for j = 1: narvs if x(i,j) < x_lb(j) x(i,j) = x_lb(j); elseif x(i,j) > x_ub(j) x(i,j) = x_ub(j); end end fit(i) = Obj_fun1(x(i,:)); if fit(i) > Obj_fun1(pbest(i,:)) pbest(i,:) = x(i,:); end if fit(i) > Obj_fun1(gbest) gbest = pbest(i,:); end end fitnessbest(d) = Obj_fun1(gbest); pause(0.1) h.XData = x; h.YData = fit; end figure(2) plot(fitnessbest) xlabel('迭代次数'); disp('最佳的位置是:'); disp(gbest) disp('此时最优值是:'); disp(Obj_fun1(gbest)) function y = Obj_fun1(x) y = 7*cos(5*x) + 4*sin(x); end
代码中有两处错误:
1. 在初始化 x 变量时,应该使用矩阵赋值,而不是两次赋值。修改后的代码如下:
```
x = x_lb + (x_ub-x_lb).*rand(n,narvs);
```
2. 在迭代更新速度时,应该将速度限制在 [-vmax, vmax] 的范围内。修改后的代码如下:
```
for j = 1: narvs
if v(i,j) < -vmax
v(i,j) = -vmax;
elseif v(i,j) > vmax
v(i,j) = vmax;
end
end
```
修改后的完整代码如下:
```
clear
f = @(x,y) 20 + x.^2 + y.^2 - 10*cos(2*pi.*x) - 10*cos(2*pi.*y);
x0 = [-5.12:0.05:5.12];
y0 = x0;
[X,Y] = meshgrid(x0,y0);
Z = f(X,Y);
figure(1);
mesh(X,Y,Z);
colormap(parula(5));
n = 10;
narvs = 2;
c1 = 0.6;
c2 = 0.6;
w_max = 0.9;
w_min = 0.4;
K = 100;
vmax = 1.2;
x_lb = -5.12;
x_ub = 5.12;
x = x_lb + (x_ub-x_lb).*rand(n,narvs);
v = -vmax + 2*vmax .* rand(n,narvs);
fit = zeros(n,1);
for i = 1:n
fit(i) = Obj_fun1(x(i,:));
end
pbest = x;
ind = find(fit == max(fit), 1);
gbest = x(ind,:);
h = scatter(x(:,1),x(:,2),80,'*r');
fitnessbest = ones(K,1);
for d = 1:K
for i = 1:n
f_i = fit(i);
f_avg = sum(fit)/n;
f_max = max(fit);
if f_i >= f_avg
if f_avg ~= f_max
w = w_min + (w_max - w_min)*(f_max - f_i)/(f_max - f_avg);
else
w = w_max;
end
else
w = w_max;
end
v(i,:) = w*v(i,:) + c1*rand(1)*(pbest(i,:) - x(i,:)) + c2*rand(1)*(gbest - x(i,:));
for j = 1: narvs
if v(i,j) < -vmax
v(i,j) = -vmax;
elseif v(i,j) > vmax
v(i,j) = vmax;
end
end
x(i,:) = x(i,:) + v(i,:);
for j = 1: narvs
if x(i,j) < x_lb
x(i,j) = x_lb;
elseif x(i,j) > x_ub
x(i,j) = x_ub;
end
end
fit(i) = Obj_fun1(x(i,:));
if fit(i) > Obj_fun1(pbest(i,:))
pbest(i,:) = x(i,:);
end
if fit(i) > Obj_fun1(gbest)
gbest = pbest(i,:);
end
end
fitnessbest(d) = Obj_fun1(gbest);
pause(0.1)
h.XData = x(:,1);
h.YData = x(:,2);
end
figure(2)
plot(fitnessbest)
xlabel('迭代次数');
disp('最佳的位置是:');
disp(gbest)
disp('此时最优值是:');
disp(Obj_fun1(gbest))
function y = Obj_fun1(x)
y = 7*cos(5*x) + 4*sin(x);
end
```
clear f = @(x,y) 20 + x.^2 + y.^2 - 10*cos(2*pi.*x) - 10*cos(2*pi.*y); x0 = [-5.12:0.05:5.12]; y0 = x0; [X,Y] = meshgrid(x0,y0); Z = f(X,Y); figure(1); mesh(X,Y,Z); colormap(parula(5)); n = 10; narvs = 2; c1 = 0.6; c2 = 0.6; w_max = 0.9; w_min = 0.4; K = 100; vmax = 1.2; x_lb = -5.12; x_ub = 5.12; x = x_lb + (x_ub-x_lb).*rand(n,narvs); v = -vmax + 2*vmax .* rand(n,narvs); fit = zeros(n,1); for i = 1:n fit(i) = Obj_fun1(x(i,:)); end pbest = x; ind = find(fit == max(fit), 1); gbest = x(ind,:); h = scatter(x(:,1),x(:,2),80,'*r'); fitnessbest = ones(K,1); for d = 1:K for i = 1:n f_i = fit(i); f_avg = sum(fit)/n; f_max = max(fit); if f_i >= f_avg if f_avg ~= f_max w = w_min + (w_max - w_min)*(f_max - f_i)/(f_max - f_avg); else w = w_max; end else w = w_max; end v(i,:) = w*v(i,:) + c1*rand(1)*(pbest(i,:) - x(i,:)) + c2*rand(1)*(gbest - x(i,:)); for j = 1: narvs if v(i,j) < -vmax v(i,j) = -vmax; elseif v(i,j) > vmax v(i,j) = vmax; end end x(i,:) = x(i,:) + v(i,:); for j = 1: narvs if x(i,j) < x_lb x(i,j) = x_lb; elseif x(i,j) > x_ub x(i,j) = x_ub; end end fit(i) = Obj_fun1(x(i,:)); if fit(i) > Obj_fun1(pbest(i,:)) pbest(i,:) = x(i,:); end if fit(i) > Obj_fun1(gbest) gbest = pbest(i,:); end end fitnessbest(d) = Obj_fun1(gbest); pause(0.1) h.XData = x(:,1); h.YData = x(:,2); endfigure(2) plot(fitnessbest)xlabel('迭代次数'); disp('最佳的位置是:'); disp(gbest)disp('此时最优值是:'); disp(Obj_fun1(gbest)) function f= Obj_fun1(x) f = @(x,y) 20 + x.^2 + y.^2 - 10*cos(2*pi.*x) - 10*cos(2*pi.*y); end
这段代码是一个简单的粒子群算法求解二元函数的最优解。具体来说,它定义了一个函数 f,代表了一个二元函数的形式;然后定义了一些参数和初始值,包括粒子数、速度范围、位置范围等;接着进行迭代计算,更新粒子的速度和位置,并记录当前最优解。最后,输出最优解和最优值。
在代码方面,建议做以下修改:
1. 增加注释:这段代码的注释较少,建议增加注释,包括变量的含义、算法逻辑等,以便于他人理解和修改。
2. 函数命名:Obj_fun1 这个函数名不太规范,建议改为 objFun1 或 obj_fun1 等符合命名规范的形式。
3. 缩进和空格:在代码风格方面,建议增加适当的缩进和空格,使代码更易读。
4. 函数传参:在函数 f 中,没有使用函数输入参数 x 和 y,这个函数可以改写为一个二元函数的形式,而不需要使用函数句柄的形式。
5. 增加结束条件:当前代码中只设置了一个迭代次数 K,建议增加结束条件,例如设置一个误差范围,当达到一定精度时停止迭代。
6. 代码结构:建议将代码分为几个函数,例如分别实现初始化、迭代、更新粒子位置等功能,以便于复用和维护。