优化后的算法请以matlab代码的形式展示
时间: 2023-07-08 17:53:07 浏览: 38
好的,下面是按进化代数递减$V_{\max}$的PSO算法的MATLAB代码。为了方便起见,我们采用了标准的Sphere函数作为测试函数。
```
function [gbest,gbest_fit] = PSO_adaptive_Vmax()
% PSO algorithm with adaptive Vmax
% Test function: Sphere function
% Parameters
n_pop = 30; % population size
n_dim = 30; % dimension of problem
n_iter = 100; % number of iterations
w = 0.8; % inertia factor
c1 = 2; % cognitive factor
c2 = 2; % social factor
Vmax_init = 10; % initial Vmax
Vmax_min = 1; % minimum Vmax
Vmax_decay_rate = 0.9; % decay rate of Vmax
% Initialization
pop = rand(n_pop,n_dim)*2-1; % particle positions
vel = rand(n_pop,n_dim)*2-1; % particle velocities
pbest = pop; % personal best
pbest_fit = zeros(n_pop,1); % personal best fitness
for i = 1:n_pop
pbest_fit(i) = sphere(pbest(i,:));
end
[gbest_fit,index] = min(pbest_fit);
gbest = pbest(index,:); % global best
% Main loop
for t = 1:n_iter
% Update Vmax
Vmax = Vmax_init*Vmax_decay_rate^t;
if Vmax < Vmax_min
Vmax = Vmax_min;
end
% Update velocity and position
for i = 1:n_pop
vel(i,:) = w*vel(i,:) + c1*rand(1,n_dim).*(pbest(i,:)-pop(i,:)) ...
+ c2*rand(1,n_dim).*(gbest-pop(i,:));
vel(i,:) = min(vel(i,:),Vmax);
vel(i,:) = max(vel(i,:),-Vmax);
pop(i,:) = pop(i,:) + vel(i,:);
% Check boundaries
pop(i,:) = min(pop(i,:),1);
pop(i,:) = max(pop(i,:),-1);
% Update personal best
fit = sphere(pop(i,:));
if fit < pbest_fit(i)
pbest(i,:) = pop(i,:);
pbest_fit(i) = fit;
end
end
% Update global best
[min_fit,index] = min(pbest_fit);
if min_fit < gbest_fit
gbest_fit = min_fit;
gbest = pbest(index,:);
end
% Print result
fprintf('Iteration %d: Best fitness = %f\n',t,gbest_fit);
end
% Test function: Sphere function
function y = sphere(x)
y = sum(x.^2);
end
end
```
需要注意的是,这里的测试函数是标准的Sphere函数,如果要测试其他函数,需要将测试函数替换为相应的函数,并修改适应度计算函数。