Calling a function in the command window (2024)

function [z_opti,v_opti,z_opts,v_opts] = GNAv2(ploco,vectors)

rng default

[ploco,pwago,vectors,~] = VehiclesToMove('SubTrain','C:\Users\nadal\Downloads\Ottimizzazione Masse Lorenzo Merlino\M2O_D3p3Project\Test\Treno3Loco.txt');

ncomb = 2*(numel(vectors)-numel(ploco));

Z=zeros(1,ncomb);

V=zeros(ncomb,length(vectors));

parfor k=1:ncomb

[~,pwago,vectors,z] = VehiclesToMove('SubTrain','C:\Users\nadal\Downloads\Ottimizzazione Masse Lorenzo Merlino\M2O_D3p3Project\Test\Treno3Loco.txt');

Z(k)=z;

V(k,:)=vectors;

end

fprintf("Fitness Function Values")

disp(Z)

fprintf("Vectors Combinations")

disp(V)

[z_opt,pos]=min(Z)

vector_opt=V(pos,:)

z_opts=z_opt;

v_opts=vector_opt;

for j=1:Inf

% Obtaining the first m/2 combinations deriving from the best

% known solution

pwago = [(2:14) (16:28)];

Copt = zeros(ncomb/2,numel(vectors));

parfor k=1:size(Copt,1)

s=pwago(randi(numel(pwago),1,2));

vector=v_opts(j,:);

vector(s(1))=v_opts(j,s(2));

vector(s(2))=v_opts(j,s(1));

% if ismember(vector,Copt,'rows') == 0

% Copt(k,:)=vector;

% else

% while ismember(vector,Copt,'rows')

% s=pwago(randi(numel(pwago),1,2));

% vector=v_opts(j,:);

% vector(s(1))=v_opts(j,s(2));

% vector(s(2))=v_opts(j,s(1));

% end

% Copt(k,:)=vector;

% end

Copt(k,:)=vector;

end

fprintf("Optimal Offspring")

disp(Copt)

%Fitness Function evaluation of the first m/2 set:

Z_1=zeros(1,size(Copt,1));

for i=1:size(Copt,1)

Settings{1} = 'TechnicalParameters=FromNominalCondition';

Settings{2,1} = ['Permute=[',num2str(Copt(i,:)),']'];

[Flong,Flong10,train,loco,T] = TrainDyPS(1,'','C:\Users\nadal\Downloads\Ottimizzazione Masse Lorenzo Merlino\M2O_D3p3Project\Test\Treno3Loco.txt',0,Settings,'Flong','Flong10','train','loco','T','NoSave');

[RLCF,RLTF] = FromLFToItsRatio(1,1,Flong10,Flong,train,loco,150,T,1,'MinLF');

z = - min(min(RLCF)) + max(max(RLTF));

Z_1(i)=z;

end

fprintf("Fitness Function of closest-to-optimal vectors")

disp(Z_1)

% The remaining m/2 combinations are now calculated, which are computed

% randomly:

[ploco,pwago,vectors,z] = VehiclesToMove('SubTrain','C:\Users\nadal\Downloads\Ottimizzazione Masse Lorenzo Merlino\M2O_D3p3Project\Test\Treno3Loco.txt');

Z_rand=zeros(1,size(Copt,1));

V_rand=zeros(size(Copt,1),length(vectors));

parfor k=1:size(Copt,1)

[ploco,pwago,vectors,z] = VehiclesToMove('SubTrain','C:\Users\nadal\Downloads\Ottimizzazione Masse Lorenzo Merlino\M2O_D3p3Project\Test\Treno3Loco.txt');

Z_rand(k)=z;

V_rand(k,:)=vectors;

end

% Regrouping into m total combinations

C_2=[Copt;V_rand]

Z_2=[Z_1 Z_rand]

z_opti=min(Z_2)

v_opti=C_2(z_opti==Z_2,:)

% Fitness Function Values comparison

if z_opti < z_opts(j)

v_opts = [v_opts; v_opti]

z_opts = [z_opts, z_opti]

else

v_opts = [v_opts; v_opts(j,:)]

z_opts = [z_opts, z_opts(j)]

end

dev=(z_opts(j)-z_opti)/z_opti

hold on

grid on

plot(z_opts,'ro')

xlabel('Iteration Number')

ylabel('Fitness Function Value')

title('Fitness Function Distribution')

if dev < 1e-4 && dev > 0 || dev == 0

break

end

end

hold off

end