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Author: JingweiToo

Main.m

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+%---------------------------------------------------------------------%
+%  Salp Swarm Algorithm (SSA) source codes demo version               %
+%---------------------------------------------------------------------%
+
+
+%---Inputs-----------------------------------------------------------
+% feat     : feature vector ( Instances x Features )
+% label    : label vector ( Instances x 1 )
+% N        : Number of salps
+% max_Iter : Maximum number of iterations
+
+%---Output-----------------------------------------------------------
+% sFeat    : Selected features (instances x features)
+% Sf       : Selected feature index
+% Nf       : Number of selected features
+% curve    : Convergence curve
+%--------------------------------------------------------------------
+
+
+%% Salp Swarm Algorithm
+clc, clear, close; 
+% Benchmark data set 
+load ionosphere.mat; 
+
+% Set 20% data as validation set
+ho = 0.2; 
+% Hold-out method
+HO = cvpartition(label,'HoldOut',ho);
+
+% Parameter setting
+N        = 10;
+max_Iter = 100; 
+
+% Salp Swarm Algorithm
+[sFeat,Sf,Nf,curve] = jSSA(feat,label,N,max_Iter,HO);
+
+% Plot convergence curve
+plot(1:max_Iter,curve);
+xlabel('Number of iterations');
+ylabel('Fitness Value');
+title('SSA'); grid on;
+
+
+
+

ionosphere.mat

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+% Notation: This fitness function is for demonstration 
+
+function cost = jFitnessFunction(feat,label,X,HO)
+if sum(X == 1) == 0
+  cost = 1;
+else
+  cost = jwrapperKNN(feat(:, X == 1),label,HO);
+end
+end
+
+
+function error = jwrapperKNN(sFeat,label,HO)
+%---// Parameter setting for k-value of KNN //
+k = 5; 
+xtrain = sFeat(HO.training == 1,:);
+ytrain = label(HO.training == 1); 
+xvalid = sFeat(HO.test == 1,:); 
+yvalid = label(HO.test == 1); 
+Model     = fitcknn(xtrain,ytrain,'NumNeighbors',k); 
+pred      = predict(Model,xvalid);
+num_valid = length(yvalid); 
+correct   = 0;
+for i = 1:num_valid
+  if isequal(yvalid(i),pred(i))
+    correct = correct + 1;
+  end
+end
+Acc   = correct / num_valid; 
+error = 1 - Acc;
+end
+

jFitnessFunction.m

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+function [sFeat,Sf,Nf,curve] = jSSA(feat,label,N,max_Iter,HO)
+lb    = 0;
+ub    = 1;
+thres = 0.5;
+
+fun = @jFitnessFunction;
+dim = size(feat,2); 
+X   = zeros(N,dim); 
+for i = 1:N
+	for d = 1:dim
+    X(i,d) = lb + (ub - lb) * rand();
+  end
+end
+% Pre
+fit   = zeros(1,N);
+fitF  = inf;
+
+curve = inf; 
+t = 1;  
+%---Iteration start----------------------------------------------------
+while t <= max_Iter
+  for i = 1:N
+    fit(i) = fun(feat,label,(X(i,:) > thres),HO);
+    if fit(i) < fitF
+      Xf   = X(i,:);
+      fitF = fit(i); 
+    end
+  end
+	c1 = 2 * exp(-(4 * t / max_Iter) ^ 2);
+	for i = 1:N
+    if i == 1
+      for d = 1:dim
+        c2 = rand(); 
+        c3 = rand();
+        if c3 >= 0.5 
+          X(i,d) = Xf(d) + c1 * ((ub - lb) * c2 + lb);
+        else
+          X(i,d) = Xf(d) - c1 * ((ub - lb) * c2 + lb);
+        end
+      end
+    else
+      for d = 1:dim
+        X(i,d) = (X(i,d) + X(i-1,d)) / 2;
+      end
+    end
+    XB = X(i,:);  XB(XB > ub) = ub;  XB(XB < lb) = lb;
+    X(i,:) = XB;
+  end
+  curve(t) = fitF;
+  fprintf('\nIteration %d Best (SSA)= %f',t,curve(t))
+  t = t + 1;
+end
+Pos   = 1:dim;
+Sf    = Pos((Xf > thres) == 1);
+Nf    = length(Sf);
+sFeat = feat(:,Sf); 
+end
+
+
+
+

jSSA.m

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+function [sFeat,Sf,Nf,curve] = jSSA2(feat,label,N,max_Iter,HO)
+lb    = 0;
+ub    = 1;
+thres = 0.5;
+
+fun = @jFitnessFunction;
+dim = size(feat,2); 
+X   = zeros(N,dim); 
+for i = 1:N
+	for d = 1:dim
+    X(i,d) = lb + (ub - lb) * rand();
+  end
+end
+% Pre
+fit   = zeros(1,N);
+fitF  = inf;
+
+curve = inf; 
+t = 1;  
+%---Iteration start----------------------------------------------------
+while t <= max_Iter
+  for i = 1:N
+    fit(i) = fun(feat,label,(X(i,:) > thres),HO);
+    if fit(i) < fitF
+      Xf   = X(i,:);
+      fitF = fit(i); 
+    end
+  end
+  % Additional sort in the first iteration to improve the 
+  % initial behavior by divide salps into leader and followers
+  if t == 1
+    [fit, idx] = sort(fit,'ascend');
+    X          = X(idx,:); 
+  end
+	c1 = 2 * exp(-(4 * t / max_Iter) ^ 2);
+	for i = 1:N
+    if i == 1
+      for d = 1:dim
+        c2 = rand(); 
+        c3 = rand();
+        if c3 >= 0.5 
+          X(i,d) = Xf(d) + c1 * ((ub - lb) * c2 + lb);
+        else
+          X(i,d) = Xf(d) - c1 * ((ub - lb) * c2 + lb);
+        end
+      end
+    else
+      for d = 1:dim
+        X(i,d) = (X(i,d) + X(i-1,d)) / 2;
+      end
+    end
+    XB = X(i,:);  XB(XB > ub) = ub;  XB(XB < lb) = lb;
+    X(i,:) = XB;
+  end
+  curve(t) = fitF;
+  fprintf('\nIteration %d Best (SSA)= %f',t,curve(t))
+  t = t + 1;
+end
+Pos   = 1:dim;
+Sf    = Pos((Xf > thres) == 1);
+Nf    = length(Sf);
+sFeat = feat(:,Sf); 
+end
+
+
+
+