jSineCosineAlgorithm.m

%[2016]-"SCA: A sine cosine algorithm for solving optimization 
%problems"

% (9/12/2020)

function SCA = jSineCosineAlgorithm(feat,label,opts)
% Parameters
lb    = 0;
ub    = 1; 
thres = 0.5; 
alpha = 2;    % constant

if isfield(opts,'T'), max_Iter = opts.T; end
if isfield(opts,'N'), N = opts.N; end
if isfield(opts,'alpha'), alpha = opts.alpha; 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
% Pre
fitD = inf; 
fit  = zeros(1,N);

curve = inf; 
t = 1;
% Iterations
while t <= max_Iter
  % Destination point
  for i = 1:N
    % Fitness
    fit(i) = fun(feat,label,(X(i,:) > thres),opts); 
    % Destination update
    if fit(i) < fitD
      fitD = fit(i);
      Xdb  = X(i,:);
    end
  end
	% Parameter r1, decreases linearly from alpha to 0 (3.4)
	r1 = alpha - t * (alpha / max_Iter);
	for i = 1:N
    for d = 1:dim
      % Random parameter r2 & r3 & r4
      r2 = (2 * pi) * rand();
      r3 = 2 * rand();
      r4 = rand();
      % Position update (3.3)
      if r4 < 0.5
      	% Sine update (3.1)
        X(i,d) = X(i,d) + r1 * sin(r2) * abs(r3 * Xdb(d) - X(i,d));
      else
        % Cosine update (3.2)
        X(i,d) = X(i,d) + r1 * cos(r2) * abs(r3 * Xdb(d) - X(i,d));
      end
    end
    % Boundary
    XB = X(i,:); XB(XB < lb) = lb; XB(XB > ub) = ub;
    X(i,:) = XB;
  end
  curve(t) = fitD;
  fprintf('\nIteration %d Best (SCA)= %f',t,curve(t))
  t = t + 1;
end
% Selects features
Pos   = 1:dim;
Sf    = Pos((Xdb > thres) == 1); 
sFeat = feat(:,Sf);
% Store results
SCA.sf = Sf; 
SCA.ff = sFeat; 
SCA.nf = length(Sf); 
SCA.c  = curve; 
SCA.f  = feat;
SCA.l  = label;
end