Matrix dimenssion must agree

Hello,

this is my code to find out the best values but getting some error please any one help me out to solve this.

function KH

clc; 
close all; 
clear all
format long
% Human Inserted %
figure;
imshow('pc3.png');
hold on
%Randomly CS are Distributed%
Mmin = 65;
Mmax = 88;
M = Mmin+rand(1,4)*(Mmax-Mmin);
Nmin = 10;
Nmax= 50;
N = Nmin+rand(1,4)*(Nmax-Nmin);
plot(M, N, 'b*');
grid on
grid on
Xmin = 58;
Xmax = 105;
XX = Xmin+rand(1,30)*(Xmax-Xmin);
Ymin = 60;
Ymax= 150;
YY = Ymin+rand(1,30)*(Ymax-Ymin);
plot(XX, YY, 'b*');
hold on;
plot(49,70, 'b*');
plot(45,110, 'b*');
plot(30,139, 'b*');
plot(138,149, 'b*');
plot(128,129, 'b*');
plot(108,89, 'b*');
hold on
Emin=56;
Emax=65;
E = Emin+rand(1,5)*(Emax-Emin);
Fmin=155;
Fmax=280;
F = Fmin+rand(1,5)*(Fmax-Fmin);
plot(E,F, 'b*');
hold on
Gmin=100;
Gmax=108;
G = Gmin+rand(1,5)*(Gmax-Gmin);
Hmin=155;
Hmax=280;
H = Hmin+rand(1,5)*(Hmax-Hmin);
plot(G,H, 'b*');
hold on
A=100;
B=140;
plot(A,B, 'mo-', 'MarkerSize', 10,'LineWidth', 2);
hold on
% plot 13 Sensor nodes%
x1 = [105,107,58,58,75,146,118,18,48,55, 90,80,80];
y1 = [190,280,190,280,145,153,103,153,103,65,85,50,10];
plot(x1, y1, 'r.','MarkerSize', 20, 'LineWidth', 2);
for i = 1:4
    x2(i) = M(i);
    y2(i) = N(i);
end
for i = 1:30
    x2(4+i) = XX(i);
    y2(4+i) = YY(i);
end
for i = 1:5
    x2(34+i) = E(i);
    y2(34+i) = F(i);
end
for i = 1:5
    x2(39+i) = G(i);
    y2(39+i) = H(i);
end
x2(45)= 49;
y2(45)= 70;
x2(46)= 45;
y2(46)=110;
x2(47)= 30;
y2(47)=139;
x2(48)= 138;
y2(48)= 149;
x2(49)= 128;
y2(49)=129;
x2(50)= 108;
y2(50)=89;
% Numbering 13 Sensor%
for i = 1:13
    O(i) = x1(i);
    P(i) = y1(i);
    text(O(i),P(i), num2str(i));
end
%Numbering 50 CS%
for i = 1:50
    O(i) = x2(i);
    P(i) = y2(i);
    text(O(i),P(i),num2str(i));
end

% Plot set 1 in red
plot(x1, y1, 'r.', 'MarkerSize', 13);
% Plot set 2 in blue
hold on;
plot(x2, y2, 'b*', 'MarkerSize', 5);
grid on;
% calculate the distance from 13 sensor to Relay%
for i=1:13
    for j=1:50
    
        % Find distances between every point in set 1 to every point in set #2.
        distances(i,j) = pdist2([x1(i),y1(i)], [x2(j), y2(j)], 'euclidean');
                
    end 
end
% find the minimum Distances%
[minDistance, indexOfClosest] = min(distances);
x1Closest = x2(indexOfClosest);
y1Closest = y2(indexOfClosest);
disp (distances);
% calculate the distance between Relay to Relay%
for i=1:50
    for j=1:50
    
        % Find distances between every point in set 1 to every point in set #2.
        dis_rly(i,j) = pdist2([x2(i),y2(i)], [x2(j), y2(j)], 'euclidean');
                
    end 
end
disp (dis_rly);
% find out the distance between Relay to sink%%
dist1 = sqrt((x2-A).^2+(y2-B).^2)
%logicalindexes = dist1 < 30.00000;
%x2Nearby = x2(logicalindexes)
%y2Nearby = y2(logicalindexes)
%disp (dist1);
% Find out the distance between Sensor to Sink %%%
dist2 =sqrt((x1-A).^2+(y1-B).^2)
logicalindexes = dist2 < 30.00000;
x1Nearby = x1(logicalindexes);
y1Nearby = y1(logicalindexes);
disp (minDistance);
% opposition based alloation %
NR=10;
MI=200;
NP=10;
NK=5;
UB = 10*ones(1,10);
LB = -10*ones(1,10);
Dt = mean(abs(UB-LB))/2; % Scale Factor
F = zeros(NP,NK);D = zeros(1,NK);V = zeros(NP,NK); %R = zeros(NP,NK);
Vf = 0.02; Dmax = 0.005; Nmax = 0.01; Sr = 0;
C_flag = 1; 
for nr = 1:NR
a=1:50;
for i = 1:5
   out(i,:) = a(randperm(numel(a),10))
   opo_out(i,:) = 51-out(i,:)
end
 %for i = 1:NP
         %for j = 1:NK
            %out(i,j) = LB(j) + (UB(j) - LB(j)).*rand(0,1);
            %opo_out(i,j) = LB(j) + (UB(j)-out(z1,z2));
         %end
    %end
        
N=10;
data_agg=1;
Er = 36.1;
s=250;
Et=16.7;
Eamp=1.97;
 % Calculate the Energy of Relay nodes % 
 for j = 1:5
    for i=1:10
        k= 1;
            if ((dist2(j))> 30.000000)
                Etf(j,i,k)= N*data_agg*Et*(s*dist1(k,out(j,i)))+N*s*data_agg*Et;
                Erf = (N-1)*Er*s;
                OEtf(j,i,k)= N*data_agg*Et*(s*dist1(k,opo_out(j,i)))+N*s*data_agg*Et;
                Ef_total(j,i,k) = Etf(j,i,k)+Erf ;
                OEf_total(j,i,k) = OEtf(j,i,k)+Erf;
            end
    end
 end
 % Calculate The Energy Of Sensor nodes %%%
 for j = 1 :5
    for i = 1:10
        for k = 1:13
            Ets(j,i,k)=s*(Et+Eamp*(distances(k,out(j,i)).^2));
            OEts(j,i,k)=s*(Et+Eamp*(distances(k,opo_out(j,i)).^2));
        end
    end
 end
disp(Ets);
disp(OEts); 
d0=30;
c=10;
tic
%Claculate the fitness %%%

for i = 1:5
   fitness(i,:)= (c*out(i))+(c*((Ets(i)*1)+Ef_total(1)))+(c*sqrt((distances(k,out(i))-d0).^2))+(c*1); 
   Ofitness(i,:)= (c*opo_out(i))+(c*((OEts(i)*1)+OEf_total(1)))+(c*sqrt((distances(k,opo_out(i))-d0).^2))+(c*1);
    
end

wtime=toc
fprintf(1, 'fitness took %f seconds to Run.\n',wtime);
disp(fitness);
disp(Ofitness);
%K = min(Fit)
i = 1:5;
if (fitness(i) > Ofitness(i))
    K(i) = Ofitness(i)
    X = opo_out
else
    K(i) = fitness(i)
    X = out
end 
%% Optimization & Simulation
 
    Kib=K;
    Xib=X;
   [Kgb(1,nr), A] = min(K)
    Xgb(:,1,nr) = X(:,A)
    
    for j = 1:MI 
        % Virtual Food
        for ll = 1:NP
           Sf = (sum(X(ll))./K);
        end
        Xf(:,j) = Sf./(sum(1./K)) %Food Location
        Kf = (c*Xf(j))+(c*((Ets(i)*1)+Ef_total(1)))+(c*sqrt((distances(k,int64(Xf(j)))-d0).^2))+(c*1);
       if 2<=j
            if Kf(j-1)<Kf(j)
                Xf(:,j) = Xf(:,j-1);
                Kf(j) = Kf(j-1);
            end
        end
        
        Kw_Kgb = max(K)-Kgb(j,nr);
        w = (0.1+0.8*(1-j/MI))
        
        for i = 1:NK
            % Calculation of distances
            Rf = Xf(:,j)-X(:,i);
            Rgb = Xgb(:,j,nr)-X(:,i);
            for ii = 1:NK
                RR(:,ii) = X(:,ii)-X(:,i);
            end
            R = sqrt(sum(RR.*RR))
            
            % % % % % % % % % % % % % Movement Induced % % % % % % % % % %
            % Calculation of BEST KRILL effect
            if Kgb(j,nr) < K(i)
                alpha_b = -2*(1+rand*(j/MI))*(Kgb(j,nr) - K(i)) /Kw_Kgb/ sqrt(sum(Rgb.*Rgb)) * Rgb
            else
                alpha_b=0;
            end
            
            % Calculation of NEIGHBORS KRILL effect
            nn=0;
            ds = mean(R)/5;
            alpha_n = 0;
            for n=1:NK
                if and(R(i)<ds,n~=i)
                    nn=nn+1;
                    if and(nn<=4,K(i)~=K(n))
                        alpha_n = alpha_n-(K(n) - K(i)) /Kw_Kgb/ R(n) * RR(:,n)
                    end
                end
            end
            disp(alpha_n);
            % Movement Induced
            V(:,i) = w*V(:,i)+Nmax*(alpha_b+alpha_n);
            
            % % % % % % % % % % % % % Foraging Motion % % % % % % % % % %
            % Calculation of FOOD attraction
            if Kf(j) < K(i)
                Beta_f=-2*(1-j/MI)*(Kf(j) - K(i)) /Kw_Kgb/ sqrt(sum(Rf.*Rf)) * Rf;
            else
                Beta_f=0;
            end
            
            % Calculation of BEST psition attraction
            Rib = Xib(:,i)-X(:,i);
            if Kib(i) < K(i)
                Beta_b=-(Kib(i) - K(i)) /Kw_Kgb/ sqrt(sum(Rib.*Rib)) *Rib;
            else
                Beta_b=0;
            end
            
            % Foraging Motion
            F(:,i) = w*F(:,i)+Vf*(Beta_b+Beta_f);
            
            % % % % % % % % % % % % % Physical Diffusion % % % % % % % % %
            D = Dmax*(1-j/MI)*floor(rand+(K(i)-Kgb(j,nr))/Kw_Kgb)*(2*rand(NP,1)-ones(NP,1));
            
            % % % % % % % % % % % % % Motion Process % % % % % % % % % % %
            DX = Dt*(V(:,i)+F(:,i));           
             
            % % % % % % % % % % % % % Crossover % % % % % % % % % % % % %
            if C_flag ==1
                C_rate = 0.8 + 0.2*(K(i)-Kgb(j,nr))/Kw_Kgb;
                Cr = rand(NP,1) < C_rate ;
                % Random selection of Krill No. for Crossover
                NK4Cr = round(NK*rand+.5);  
                % Crossover scheme
                X(:,i)=X(:,NK4Cr).*(1-Cr)+X(:,i).*Cr;
            end
            
            % Update the position
            X(:,i)=X(:,i)+DX;
            %X(:,i)=findlimits(X(:,i),LB,UB,Xgb(:,j,nr)); % Bounds Checking

            K(i)=(c*X(i))+(c*((Ets(i)*1)+Ef_total(1)))+(c*sqrt((distances(k,int64(X(i)))-d0).^2))+(c*1);;
            if K(i)<Kib(i)
                Kib(i)=K(i);
                Xib(:,i)=X(:,i);
            end         
        end
        
        % Update the current best
        [Kgb(j+1,nr), A] = min(K);
        if Kgb(j+1,nr)<Kgb(j,nr)
            Xgb(:,j+1,nr) = X(:,A);
        else
            Kgb(j+1,nr) = Kgb(j,nr);
            Xgb(:,j+1,nr) = Xgb(:,j,nr);
        end
    end
end

%% Post-Processing
[Best, Ron_No] = min(Kgb(end,:))
Xgb(:,end,Ron_No)
Mean = mean(Kgb(end,:))
Worst = max(Kgb(end,:))
Standard_Deviation = std(Kgb(end,:))

Thank you

[KSSV]

First you read about pre-allocation.......all variables are not pre-allocated.....

[Sushree Patra]

Thank you

can you please send me any specific link or I just google it.

[Sushree Patra]

after I preallocate Kgb = zeros(1,NR); Xgb = zeros(1,NR); K = zeros(NK,1); in line 152 i got this kind of error shown in bellow

Assignment has more non-singleton rhs dimensions than non-singleton subscripts

Error in test (line 225)

Xgb(:,1,nr) = X(:,A)