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jSymbioticOrganismsSearch.m
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jSymbioticOrganismsSearch.m
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%[2014]-"Symbiotic organisms search: A new metaheuristic optimization
%algorithm"
% (9/12/2020)
function SOS = jSymbioticOrganismsSearch(feat,label,opts)
% Parameters
lb = 0;
ub = 1;
thres = 0.5;
if isfield(opts,'N'), N = opts.N; end
if isfield(opts,'T'), max_Iter = opts.T; end
if isfield(opts,'thres'), thres = opts.thres; end
% Objective function
fun = @jFitnessFunction;
% Number of dimensions
dim = size(feat,2);
% Initial
X = zeros(N,dim);
for i = 1:N
for d = 1:dim
X(i,d) = lb + (ub - lb) * rand();
end
end
% Fitness
fit = zeros(1,N);
fitG = inf;
for i = 1:N
fit(i) = fun(feat,label,(X(i,:) > thres),opts);
% Global best
if fit(i) < fitG
fitG = fit(i);
Xgb = X(i,:);
end
end
% Pre
Xi = zeros(1,dim);
Xj = zeros(1,dim);
curve = zeros(1,max_Iter);
curve(1) = fitG;
t = 2;
% Iteration
while t <= max_Iter
for i = 1:N
% {1} Mutualism phase
R = randperm(N); R(R == i) = [];
J = R(1);
% Benefit factor [1 or 2]
BF1 = randi([1,2]);
BF2 = randi([1,2]);
for d = 1:dim
% Mutual vector (3)
MV = (X(i,d) + X(J,d)) / 2;
% Update solution (1-2)
Xi(d) = X(i,d) + rand() * (Xgb(d) - MV * BF1);
Xj(d) = X(J,d) + rand() * (Xgb(d) - MV * BF2);
end
% Boundary
Xi(Xi > ub) = ub; Xi(Xi < lb) = lb;
Xj(Xj > ub) = ub; Xj(Xj < lb) = lb;
% Fitness
fitI = fun(feat,label,(Xi > thres),opts);
fitJ = fun(feat,label,(Xj > thres),opts);
% Update if better solution
if fitI < fit(i)
fit(i) = fitI;
X(i,:) = Xi;
end
if fitJ < fit(J)
fit(J) = fitJ;
X(J,:) = Xj;
end
% {2} Commensalism phase
R = randperm(N); R(R == i) = [];
J = R(1);
for d = 1:dim
% Random number in [-1,1]
r1 = -1 + 2 * rand();
% Update solution (4)
Xi(d) = X(i,d) + r1 * (Xgb(d) - X(J,d));
end
% Boundary
Xi(Xi > ub) = ub; Xi(Xi < lb) = lb;
% Fitness
fitI = fun(feat,label,(Xi > thres),opts);
% Update if better solution
if fitI < fit(i)
fit(i) = fitI;
X(i,:) = Xi;
end
% {3} Parasitism phase
R = randperm(N); R(R == i) = [];
J = R(1);
% Parasite vector
PV = X(i,:);
% Randomly select random variables
r_dim = randperm(dim);
dim_no = randi([1,dim]);
for d = 1:dim_no
% Update solution
PV(r_dim(d)) = lb + (ub - lb) * rand();
end
% Boundary
PV(PV > ub) = ub; PV(PV < lb) = lb;
% Fitness
fitPV = fun(feat,label,(PV > thres),opts);
% Replace parasite if it is better than j
if fitPV < fit(J)
fit(J) = fitPV;
X(J,:) = PV;
end
end
% Update global best
for i = 1:N
if fit(i) < fitG
fitG = fit(i);
Xgb = X(i,:);
end
end
curve(t) = fitG;
fprintf('\nIteration %d GBest (SOS)= %f',t,curve(t))
t = t + 1;
end
% Select features based on selected index
Pos = 1:dim;
Sf = Pos((Xgb > thres) == 1);
sFeat = feat(:,Sf);
% Store results
SOS.sf = Sf;
SOS.ff = sFeat;
SOS.nf = length(Sf);
SOS.c = curve;
SOS.f = feat;
SOS.l = label;
end