function [x_opt,y_opt,W]=PSO(epsilon,Nmax,xmin,xmax)

W=0;
N=2; %wymiar problemu, tu dla dwóch zmiennych
C=20;
w=0.8; % wartosc powinna malec w czasie dzialania algorytmu
c1=1;
c2=1;

R.x=zeros(N,C);
R.v=zeros(N,C);
R.y=zeros(1,C);

for i=1:C
    R.x(:,i)=(xmax-xmin).*rand(N,1)+xmin;
    R.v(:,i)=0.02*(xmax-xmin).*rand(N,1)-0.01*(xmax-xmin);
    [R.y(i),W] = fit_fun(R.x(:,i),W);
end

x_opt_i=R.x;
y_opt_i=R.y;
[y_opt,numer]=min(R.y);
x_opt=R.x(:,numer);   %najlepsze rozwiązanie to cząstka o numerze ...

while true
    for i=1:C
        R.v(:,i)=w*R.v(:,i)+c1*rand*(x_opt-R.x(:,i))+...
            c2*rand*(x_opt_i(:,i)-R.x(:,i));
        R.x(:,i)=R.x(:,i)+R.v(:,i);
        [R.y(i),W]=fit_fun(R.x(:,i),W);
        if R.y(i)<y_opt
            y_opt=R.y(i);
            x_opt=R.x(:,i);
            if y_opt<epsilon
                return;
            end
        end
        if R.y(i) <y_opt_i(i)
            y_opt_i(i)=R.y(i);
            x_opt_i(:,i)=R.x(:,i);
        end
        if W>Nmax
            return;
        end
    end
end


function [y,w]=fit_fun(x,w)

w=w+1;
%y=x(1)^2+x(2)^2;
m1=400;
m2=50;
k1=x(1);
k2=2e5;
b=x(2);
g=9.81;
x2_0=-(m1+m2)*g/k2;
x1_0=-m1*g/k1+x2_0;
T=5;
dt=0.01;
A=0.06;
f=2*pi*10;

assignin('base','m1',m1);
assignin('base','m2',m2);
assignin('base','k1',k1);
assignin('base','k2',k2);
assignin('base','b',b);
assignin('base','g',g);
assignin('base','x1_0',x1_0);
assignin('base','x2_0',x2_0);
assignin('base','T',T);
assignin('base','dt',dt);
assignin('base','a',A);
assignin('base','f',f);
sim('model.mdl');
plot(t,x1);
figure;

[l,m]=butter(12,0.16,'low');
x1_f=filter(l,m,x1);
plot(t,x1_f);

y=max(abs(x1_f));
y=y+1e6*p;
p=max(0,-k1)^2+max(0,k1-2e4)^2+ ...
    max(0,b)^2+max(0,b-3e3)^2+ ...
    max(0,-x1_0-0.3)^2;
plot(t,x1);




error('To by było na tyle');