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jACO.m
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jACO.m
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function [sFeat,Nf,Sf,curve] = jACO(feat,label,N,max_Iter,tau,eta,...
alpha,beta,rho,HO)
% Objective function
fun = @jFitnessFunction;
% Number of dimensions
dim = size(feat,2);
% Initial Tau & Eta
tau = tau * ones(dim,dim);
eta = eta * ones(dim,dim);
% Pre
fitG = inf;
fit = zeros(1,N);
curve = inf;
t = 1;
%---Iterations start--------------------------------------------------
while t <= max_Iter
% Reset ant
X = zeros(N,dim);
for i = 1:N
% Random number of features
num_feat = randi([1,dim]);
% Ant start with random position
X(i,1) = randi([1,dim]);
k = [];
if num_feat > 1
for d = 2:num_feat
% Start with previous tour
k = [k(1:end), X(i, d-1)];
% Edge/Probability Selection
P = (tau(k(end),:) .^ alpha) .* (eta(k(end),:) .^ beta);
% Set selected position = 0 probability
P(k) = 0;
% Convert probability
prob = P ./ sum(P(:));
% Roulette Wheel selection
route = jRouletteWheelSelection(prob);
% Store selected position to be next tour
X(i,d) = route;
end
end
end
% Binary
X_bin = zeros(N,dim);
for i = 1:N
% Binary form
ind = X(i,:);
ind(ind == 0) = [];
X_bin(i,ind) = 1;
end
% Fitness
for i = 1:N
fit(i) = fun(feat,label,X_bin(i,:),HO);
% Global update
if fit(i) < fitG
Xgb = X(i,:);
fitG = fit(i);
end
end
%---// [Pheromone update rule on tauK] //
tauK = zeros(dim,dim);
for i = 1:N
% Update Phromones
tour = X(i,:);
tour(tour == 0) = [];
% Number of features
len_x = length(tour);
tour = [tour(1:end), tour(1)];
for d = 1:len_x
% Feature selected on graph
x = tour(d);
y = tour(d + 1);
% Update delta tau k on graph
tauK(x,y) = tauK(x,y) + (1 / (1 + fit(i)));
end
end
%---// [Pheromone update rule on tauG] //
tauG = zeros(dim,dim);
tour = Xgb;
tour(tour == 0) = [];
% Number of features
len_g = length(tour);
tour = [tour(1:end), tour(1)];
for d = 1:len_g
% Feature selected on graph
x = tour(d);
y = tour(d + 1);
% Update delta tau G on graph
tauG(x,y) = 1 / (1 + fitG);
end
%---// Evaporate pheromone //
tau = (1 - rho) * tau + tauK + tauG;
% Save
curve(t) = fitG;
fprintf('\nIteration %d Best (ACO)= %f',t,curve(t))
t = t + 1;
end
% Select features based on selected index
Sf = Xgb;
Sf(Sf == 0) = [];
sFeat = feat(:,Sf);
Nf = length(Sf);
end
function Index = jRouletteWheelSelection(prob)
C = cumsum(prob);
P = rand();
for i = 1:length(C)
if C(i) > P
Index = i;
break;
end
end
end