?? pso.m
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%------基本粒子群優化算法(Particle Swarm Optimization)-----------
function result = PSO(dim)
%--------聲明全局---------------------------
global N;
global m;
global MaxDT;
global Qmax;
global Kmax;
global w;
global c1;
global c2;
global curpoint;
global segR;
global pathta;
global pamoveta;
global numta;
global pathpoint;
global hadsteps;
global curstep;
global pointIndex;
%-----------------------
%------初始化種群的個體(可以在這里限定位置和速度的范圍)------------
result = true;
%psosued = ones(1,N); %粒子群成功
Xmax = dim*pathta; %最大x值
Xmin = (dim-1)*pathta; %最小x值
Vmax = (Xmax - Xmin)*0.1;
for i=1:N
[temp,psosued(i)] = initX(Xmax,Xmin);
%如果初始化不成功則不做任何的v變化
if psosued(i)
x(i,:) = temp;
for j=1:m
v(i,j) = -Vmax+2*rand*Vmax;
end
end
end
%判斷是否有條可走路徑
for i=1:N
if psosued(i)
break;
end
end
if psosued(i) == false
result = false;
return;
end
%------先計算各個粒子的適應度,并初始化Pi和Pg----------------------
%如果最大的
best_fitness = 10000;
for i=1:N
%如果粒子群初始化不成功的話將其適應值改為10000
if psosued(i)
p(i)=fitness(x(i,:),m);
y(i,:)=x(i,:); %y記錄前一次迭代,粒子的位置
if p(i)<best_fitness
pg = x(i,:);
best_fitness = p(i);
end
end
end
%------進入主要循環,按照公式依次迭代,直到滿足精度要求------------
for t=1:MaxDT
for i=1:N
%如果psosued為false的話,就讓這個粒子不迭代
if psosued(i)
%----cjss更新x
for q = 1:Qmax
%保護現場如果不通,直接返回
yuanx = x(i,:);
yuanv = v(i,:);
for j = 1:m
for k=1:Kmax
%保護現場,如果更新后不連通,返回原來的x,v值
tempv = v(i,j);
tempx = x(i,j);
%%%粒子群更新公式%%%%%%%%%%%
v(i,j)=w*v(i,j)+c1*rand*(y(i,j)-x(i,j))...
+c2*rand*(pg(j)-x(i,j));
%看v是否越界
if v(i,j)>Vmax
v(i,j) = Vmax;
else if v(i,j)<-Vmax;
v(i,j) = -Vmax;
end
end
x(i,j)=x(i,j)+v(i,j);
%看x是否越界
if x(i,j)>Xmax
x(i,j) = Xmax;
else if x(i,j)<Xmin
x(i,j) = Xmin;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%
%如果j是1的話就將curpoint賦值給purPoint
%如果不是的話講將前一個的tempPoint賦值給purPoint
if 1 == j
purPoint = curpoint;
else
purPoint = tempPoint;
end
%將極坐標轉換為直角坐標
[tempPoint(1),tempPoint(2)]=plorTozhijiao(j*segR,x(i,j));
%判斷這個點和前面的點是否相通
r = Conn(purPoint(1),purPoint(2),tempPoint(1),tempPoint(2));
if r
break;
else
v(i,j) = tempv;
x(i,j) = tempx;
end
end
%如果k太大的話,說明此路不通,直接重新開始
if r == false
break ;
end
end
if r
break;
else
%保護現場如果不通,直接返回
x(i,:)=yuanx;
v(i,:)=yuanv;
end
end
%---更新x
% x(i,:)=x(i,:)+v(i,:);
if fitness(x(i,:),m)<p(i)
p(i)=fitness(x(i,:),m);
y(i,:)=x(i,:);
end
if p(i)<fitness(pg,m)
pg=y(i,:);
end
end
end
%記錄每次迭代的最好fitness
Pbest(t)=fitness(pg,m);
end
%----------更新鏈表
%將這一維的字賦值維2,然后初始化下一個節點
pamoveta(curstep,dim) = 2;%2表示已經走過了
curstep = curstep+1;%將走的點賦值給當前值,繼續搜索
pamoveta(curstep,:)= 1;
%將下一段的維值
tempta = mod(numta/2+dim,numta);
if tempta == 0
tempta = 6;
end
pamoveta(curstep,tempta) = 3;%3表示來的路
%------------
%------更新點-------------
for j=1:m
[tempPoint(1),tempPoint(2)]=plorTozhijiao(j*segR,pg(j));
pointIndex = pointIndex+1;
pathpoint(pointIndex,1) = tempPoint(1); %路徑規劃得出的點
pathpoint(pointIndex,2) = tempPoint(2);
end
pathpoint
curpoint(1) = tempPoint(1);
curpoint(2) = tempPoint(2);
hadsteps(curstep,1) = curpoint(1);
hadsteps(curstep,2) = curpoint(2);
% %------最后給出計算結果
%
% disp('*************************************************************')
% disp('函數的全局最優位置為:')
% Solution=pg'
% disp('最后得到的優化極值為:')
Result=fitness(pg,m)
% disp('*************************************************************')
%
% %------算法結束---DreamSun GL & HF-----------------------------------
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