مشکل در متلب

[helen66 435]

با سلام و وقت بخیر

چطور میتونم سرعت اجرای ی برنامه تو متلب رو بالا ببرم؟

با سپاس

[ehsan_faal 561]

سلام.

1-اجتناب از حلقه ها

2- استفاده از profiler

3-در صورت امکان استفاده از mex

4-گذاشتن کد رو این سایت و هم فکری با سایر دوستان

[helen66 435]

با سلام و وقت بخیر

ممنون از پاسخ مفیدتون...تعداد کدها زیاده اونها رو بصورت مجزا قرار میدم ممنون میشم کمکم کنید،کد نهایی espso هستش که سرعتش پایینه:

function [PLOSS V]=foku(xx)
countnum=0;
BranchData = [
   1   2   1.00922   1.00470;
   2   3   1.004930  1.002511;
   3   4   0.003660  0.1864;
   4   6   0.03811  0.1941;
   4   5   0.03811  0.1941;
   6   7   0.1872  0.6188;
   7   8   0.7114  0.2351;
   8   9   1.0300  0.7400;
   9   10  1.0440  0.7400;
   10  11  0.0966  0.0650;
   11  12  0.0744  0.1238;
   9   13  0.0680  1.1550;
   13  14  0.0416  0.7129;
   13  15  0.0910  0.5260;
   15  16  0.0463  0.5450;
   16  17  0.2890  1.7210;
   17  18  0.7320  0.5740;
   17  19  0.1640  0.1565;
   19  20  0.5042  1.3554;
   20  21  0.4095  0.4784;
   21  22  0.7089  0.9373;
   20  23  0.4512  0.3083;
   23  24  0.8980  0.7091;
   23  25  0.8960  0.7011;
   25  26  0.2030  0.1034;
   26  27  0.2842  0.1447;
   27  28  0.0590  0.9337;
   28  29  0.8042  0.7006;
   25  30  0.5075  0.2585;
   30  31  0.9744  0.9630;
   31  32  0.3105  0.3619;
   31  33  0.3410  0.5302;
   33  34  0.3410  0.5302;  
];

NodeData = [
   2   100.00  60.00;
   3   90.00   40.00;
   4   80.00  80.00;
   5   60.00   30.00;
   6   60.00   80.00;
   7   80.00  80.00;
   8   80.00  90.00;
   9   60.00   20.00;
   10  60.00   20.00;
   11  45.00   80.00;
   12  60.00   65.00;
   13  60.00   65.00;
   14  120.00  80.00;
   15  60.00   60.00;
   16  60.00   60.00;
   17  60.00   60.00;
   18  90.00   40.00;
   19  90.00   40.00;
   20  90.00   40.00;
   21  90.00   40.00;
   22  90.00   40.00;
   23  90.00   50.00;
   24  80.00  10.00;
   25  80.00  10.00;
   26  60.00   65.00;
   27  60.00   65.00;
   28  60.00   60.00;
   29  80.00  70.00;
   30  100.00  30.00;
   31  150.00  70.00;
   32  110.00  100.00;
   33  60.00   40.00;
   34  60.00   40.00;
];                                     

UB = 12.66;                            
SB = sum(xx) ;                               
ZB = UB^2/SB;                           

BranchData(:,[3,4]) = BranchData(:,[3,4]) / ZB;     
NodeData(:,[2,3]) = NodeData(:,[2,3]) / SB / 1800;

NN = 34;                               

A0 = zeros(NN);
for n = 1:NN-1
   A0(BranchData(n,1),BranchData(n,2)) = 1;
end                                    

AssociatedMatrix=0;

for n=2:NN-1
   AssociatedMatrix(n,n)=1;
   temp=BranchData(n-1,1);
   AssociatedMatrix(n,1:n-1)=AssociatedMatrix(temp,1:n-1);
end

A0T = A0';                              
S = [0;-NodeData(:,2) - 1i*NodeData(:,3)];      
ZL = [0;BranchData(:,3) + 1i*BranchData(:,4)];       

V = ones(NN,1);
V(1) = 1;                           

IL(NN,1) = -conj(S(NN) / V(NN));          

Delta = 1;                             
TempV = V;                             

while Delta > 1e-3
   countnum=countnum+1;
   IN = conj(S ./ V);                    
   
   for n = 1:NN-1
       IL(NN-n) = A0(NN-n,NN-n+1:end) * IL(NN-n+1:end) - IN(NN-n);
   end                                 
   
   for n = 2:NN
       V(n) = A0T(n,1:n-1) * V(1:n-1) - ZL(n) * IL(n);
   end                                 
   
   Delta = max(abs(V-TempV));          
   TempV = V;                         
end

Vangle(:,1)=abs(V);
Vangle(:,2)=angle(V)/3.1415*180;

for i=1:NN-1
   st=BranchData(i,1);
   en=BranchData(i,2);
   Sij(i,1)=V(st)*conj((V(st)-V(en))/ZL(i+1));
   Sji(i,1)=V(en)*conj((V(en)-V(st))/ZL(i+1));
end
PLOSS=sum(real(Sij))*1000-(SB/1.2);

end

[helen66 435]

کد espso

clc
clear;
close all;

%% Problem Definition

global NFE;

CostFunction=@(Prate) cost222(Prate);        % Cost Function

nVar=24;             % Number of Decision Variables

VarSize=[1 nVar];   % Size of Decision Variables Matrix

        % Lower Bound of Variables
VarMax=6 ;         % Upper Bound of Variables
PL=[1.7 1.5 1.3 1.32 1.4 1.52 1.6 1.9 2.1 2.25 2.4 2.5 2.55 2.56 2.59 2.55 ...
   2.8 3.2 3.5 3.7 3.73 3.4 3 2.3];
VarMin=PL ;

%% PSO Parameters

MaxIt=300;      % Maximum Number of Iterations

nPop=30;        % Population Size (Swarm Size)

wmax=1;            % Inertia Weight
wmin=0.2;     % Inertia Weight Damping Ratio
c1=2;           % Personal Learning Coefficient
c2=2;           % Global Learning Coefficient

% Constriction Coefficients
% phi1=2.05;
% phi2=2.05;
% phi=phi1+phi2;
% chi=2/(phi-2+sqrt(phi^2-4*phi));
% w=chi;          % Inertia Weight
% wdamp=1;        % Inertia Weight Damping Ratio
% c1=chi*phi1;    % Personal Learning Coefficient
% c2=chi*phi2;    % Global Learning Coefficient

% Velocity Limits
VelMax=0.1*(VarMax-VarMin);
VelMin=-VelMax;

%% Initialization

empty_particle.Position=[];
empty_particle.Cost=[];
empty_particle.Velocity=[];
empty_particle.Best.Position=[];
empty_particle.Best.Cost=[];

particle=repmat(empty_particle,nPop,1);

GlobalBest.Cost=inf;

for i=1:nPop
   Prate=randsrc(1,1,[.001:.01:.2])*PL+PL;
   % Initialize Position
   particle(i).Position=Prate;
   
   % Initialize Velocity
   particle(i).Velocity=zeros(VarSize);
   
   % Evaluation
   particle(i).Cost=CostFunction(particle(i).Position);
   % Update Personal Best
   particle(i).Best.Position=particle(i).Position;
   particle(i).Best.Cost=particle(i).Cost;
   
   % Update Global Best
   if particle(i).Best.Cost<GlobalBest.Cost
       
       GlobalBest=particle(i).Best;
       
   end
   
end
disp('aaaa')
BestCost=zeros(MaxIt,1);

nfe=zeros(MaxIt,1);
w=1;

%% PSO Main Loop

for it=1:MaxIt
   
   for i=1:nPop
       
       % Update Velocity
       particle(i).Velocity = w*particle(i).Velocity ...
           +c1*rand(VarSize).*(particle(i).Best.Position-particle(i).Position) ...
           +c2*rand(VarSize).*(GlobalBest.Position-particle(i).Position);
       
       % Apply Velocity Limits
       particle(i).Velocity = max(particle(i).Velocity,VelMin);
       particle(i).Velocity = min(particle(i).Velocity,VelMax);
       
       % Update Position
       particle(i).Position = particle(i).Position + particle(i).Velocity;
       
       % Velocity Mirror Effect
%         IsOutside=(particle(i).Position<VarMin | particle(i).Position>VarMax);
%         particle(i).Velocity(IsOutside)=-particle(i).Velocity(IsOutside);
%         
       % Apply Position Limits
       particle(i).Position = max(particle(i).Position,VarMin);
       particle(i).Position = min(particle(i).Position,VarMax);

%for iv=1:24
    % particle(iv).Position(iv)= PL(iv)+rand*1e2;
%end

       % Evaluation
       particle(i).Cost = CostFunction(particle(i).Position);
       
       % Update Personal Best
       if particle(i).Cost<particle(i).Best.Cost;
           
           particle(i).Best.Position=particle(i).Position;
           particle(i).Best.Cost=particle(i).Cost;
           
           % Update Global Best
           if particle(i).Best.Cost<GlobalBest.Cost
               
               GlobalBest=particle(i).Best;
               
           end
           
       end
       
   end
   
   BestCost(it)=GlobalBest.Cost;

   
   disp(['Iteration ' num2str(it) ', Best Cost = ' num2str(BestCost(it))]);
   
   w=wmax-it*((wmax-wmin)/MaxIt);
   
end

%% Results
cost1 =  zzz(GlobalBest.Position );
figure;
plot([1:300],BestCost,'LineWidth',2);

xlabel('Iteration');
ylabel('Best Cost');
[PLOSS V]=foku(GlobalBest.Position);
figure;
plot([1:34],V,'-*');

xlabel('BUS');
ylabel('Voltage');

[helen66 435]

کد cos222

function cost1 =  cost222(Prate )
%UNTITLED3 Summary of this function goes here
%   Detailed explanation goes here
% daily travel distance
beta1=0.1;
beta2=0.2;
alfa=10;
t=1:24;
a=readfis('gu1');
%Read a fuzzy inference system from a gu1
x=normrnd(9.97,2.2,24,1);       %depend on arrival time
y=normrnd(17.01,3.2,24,1);

%depond on departure time
d=evalfis([x y],a);
%daily travel distance
aer=[30,40,60];                       %DIFFERENT AERS
dr1=aer(randi([1,3],1)); 
landa=d./100;                     %percentage of mileage driven in all electrical...
% mode

if (landa.*d)>zeros(24,1),(landa.*d)<(0.8.*dr1);
   socinitial=(1-(landa.*d)./dr1)*100;
elseif (landa*d)>=0.8*dr1;
   socinitial=0.2;
end
%BATTERY CAPACITY FOR DIFFERENT TYPES OF PHEV (kWh)
h1=[7.8,9,11.4,13.8];
p1=h1(randi([1,4],1,1));
h2=[10.4,12,15.2,18.4];
p2=h2(randi([1,4],1,1));
h3=[15.6,18,22.8,27.6];
p3=h3(randi([1,4],1,1));
p=[p1 p2 p3];
eta=0.85+(0.9-0.85)*rand(1,24);            % the charging efficiency factor
c=p(randi(1,1,24));                          %BATTERY CAPACITY
ereq=((1-socinitial).*c')./eta';            %The energy required to fulfill the battery
%Smart Pricing Policy

psys=ereq'./t;                                  %load demand of the system at time slot
pavg=ereq./24;                                   %the average load demand of the system

% disp(rt);
% kd1=eye(1,100);
k=[-1,0,1];
kd=k(randi([1,3],1,24 ));                     %charging strategy            %the rated charging power of the dth PHEV
for pp=1:24
rt(pp)=beta1+(beta2*(alfa.^((psys(pp)-pavg(pp))./pavg(pp))));
end

for t=1:24
   coschg(t)=abs( kd(t)*Prate(t).*rt(t));                     %charging cost
end

% disp(coschg)

%Battery Degradation Cost
cl=240;
lc=5000;
% etadis=0.8;
cb=300;
% Edis=300;
DOD=0.8;
h1=[7.8,9,11.4,13.8,10.4,12,15.2,18.4,15.6,18,22.8,27.6];
Eb=h1(randi([1,12],1,24));

% kd1=eye(1,100);
k=[-1,0,1];
kd=k(randi([1,3],1,24 ));
Dd=zeros(1,24);
Dd(kd==-1)=1;                              %discharging state of PHEVs

   for t=1:24
       Edis(t)=Dd(t).*Prate(t);
       costbat(t)=abs((((cb*Eb(t))+cl)/((lc*DOD)*Eb(t)))*Edis(t));%discharge energy by PHEVs
   end

    %Battery Degradation Cost
% disp(costbat);                     
reg=.4928;                                   %regulation service price at time slot
% kd1=eye(1,100);
k=[-1,0,1];
kd=k(randi([1,3],1,24 ));                 %charging strategy       %the rated charging power of the dth PHEV
Id=zeros(1,24);                            %idle state of PHEVs
Id(kd==0)=1;

                     %the frequency regulation capacity of the system
Earnreg=0;
for t=1:24
   pr(t)=Id(t).*Prate(t);
   Earnreg=Earnreg+abs(pr(t).*reg);   %revenue earned by regulation service
end
qq=sum(coschg);
qqq=sum(costbat);
qqqq=Earnreg;
% Earnreg1=Earnreg.*eye(1,100);              
cost1=sum(coschg)+sum(costbat)-Earnreg;

end

[helen66 435]

با سلام

دوستان کسی نتونست مشکل بالا رو حل کنه؟؟؟

[hfml 1545]

سلام

متاسفانه وقت ندارم کدتون رو بخونم ولی به طور کلی همون جور که دوستمون گفتن، اگه بتونین به جای حلقه، از بردار یا ماتریس استفاده کنید خوبه. تو اینترنت جستجو کنید: VECTORIZATION IN MATLAB

 

این لینک رو هم نگاه کنید. البته خودم امتحان نکردم این روشو ولی به نظر خوب میاد.

 

از پردازش موازی و gpu هم میتونید استفاده کنید ولی کدتون رو باید درست بنویسید گویا تا بشه از اینا استفاده کرد. مسئله تون هم باید قابلیت موازی سازی داشته باشه.

[asadiiii 0]

سلام بزرگوار خوبی شما میگم میتونین یک سوال رو راجب متلب بهم کمک کنین عکس سوال رو پایین بارگذاری کردم

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