format long;
N=60;
M=100;
D=30;
w=1;
c1=1.49445;
c2=1.49445;
%------初始化种群的个体------------
S=59;
D=30;
for j=1:D
x0(1,j)=2*(rand-0.5);
v0(1,j)=2*(rand-0.5);
for i=1:S
x0(i+1,j)=sin(2./x0(i,j));
v0(i+1,j)=sin(2./v0(i,j));
end
end
x=100.*x0;
v=5.*v0;
%------先计算各个粒子的适应度，并初始化Pi和Pg----------------------
for i=1:N
p(i)=fitness(x(i,:));
y(i,:)=x(i,:);
end
pg = x(N,:); %Pg为全局最优
for i=1:(N-1)
if fitness(x(i,:))<fitness(pg)
pg=x(i,:);
end
end
%------进入主要循环，按照公式依次迭代------------
for t=1:M
for i=1:N
v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:))+c2*rand*(pg-x(i,:));
for k=1:D
if v(i,k)<-5
v(i,k)=-5;
end
if v(i,k)>5
v(i,k)=5;
end
end
x(i,:)=x(i,:)+v(i,:);
for k=1:D
if x(i,k)<-100
x(i,k)=-100;
end
if x(i,k)>100
x(i,k)=100;
end
end
if fitness(x(i,:))<p(i)
p(i)=fitness(x(i,:));
y(i,:)=x(i,:);
end
if p(i)<fitness(pg)
pg=y(i,:);
end
end
Pbest(t)=fitness(pg);
end
xm = pg';
fv = fitness(pg);
plot(Pbest);
xlabel('迭代次数');
ylabel('粒子全局最优适应度')
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