Add files via upload

Author: JingweiToo

Main.m

@@ -0,0 +1,50 @@
+%---------------------------------------------------------------------%
+%  Whale Optimization Algorithm (WOA) source codes demo version       %
+%---------------------------------------------------------------------%
+
+
+%---Inputs-----------------------------------------------------------
+% feat     : feature vector ( Instances x Features )
+% label    : label vector ( Instances x 1 )
+% N        : Number of whales
+% max_Iter : Maximum number of iterations
+% b        : Constant 
+
+%---Output-----------------------------------------------------------
+% sFeat    : Selected features (instances x features)
+% Sf       : Selected feature index
+% Nf       : Number of selected features
+% curve    : Convergence curve
+%--------------------------------------------------------------------
+
+
+%% Whale Optimization 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; 
+
+% Whale Optimization Algorithm
+[sFeat,Sf,Nf,curve] = jWOA(feat,label,N,max_Iter,HO);
+
+% Accuracy
+Acc = jKNN(sFeat,label,HO); 
+fprintf('\n Accuracy: %g %%',Acc); fprintf('\n');
+
+% Plot convergence curve
+plot(1:max_Iter,curve); 
+xlabel('Number of Iterations');
+ylabel('Fitness Value');
+title('WOA'); grid on;
+
+
+
+

ionosphere.mat

@@ -0,0 +1,31 @@
+% 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

@@ -0,0 +1,20 @@
+
+function Acc = jKNN(feat,label,HO)
+%---// Parameter setting for k-value of KNN //
+k=5; 
+
+xtrain = feat(HO.training==1,:);  ytrain = label(HO.training==1); 
+xvalid = feat(HO.test==1,:);      yvalid = label(HO.test==1); 
+Model  = fitcknn(xtrain,ytrain,'NumNeighbors',k); 
+ypred  = predict(Model,xvalid);
+
+num_valid = length(yvalid);
+correct   = 0;
+for i = 1:num_valid
+  if isequal(yvalid(i),ypred(i))
+    correct = correct + 1;
+  end
+end
+Acc = 100 * (correct / num_valid); 
+end
+

jKNN.m

@@ -0,0 +1,77 @@
+
+function [sFeat,Sf,Nf,curve] = jWOA(feat,label,N,max_Iter,HO)
+% Parameters
+lb  = 0; 
+ub  = 1;
+b   = 1;
+
+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
+fit  = zeros(1,N);
+fitG = inf;
+for i = 1:N
+  fit(i) = fun(feat,label,(X(i,:) > 0.5),HO);
+  if fit(i) < fitG
+    fitG = fit(i); 
+    Xgb  = X(i,:);
+  end
+end
+
+curve = inf;
+t = 1; 
+%---Iteration start---------------------------------------------------
+while t <= max_Iter
+  a = 2 - t * (2 / max_Iter);
+  for i = 1:N
+    A = 2 * a * rand() - a;
+    C = 2 * rand();
+    p = rand(); 
+    l = -1 + 2 * rand();  
+    if p  < 0.5
+      if abs(A) < 1
+        for d = 1:dim
+          Dx     = abs(C * Xgb(d) - X(i,d));
+          X(i,d) = Xgb(d) - A * Dx;
+        end
+      elseif abs(A) >= 1
+        for d = 1:dim
+          k      = randi([1,N]);
+          Dx     = abs(C * X(k,d) - X(i,d));
+          X(i,d) = X(k,d) - A * Dx;
+        end
+      end
+    elseif p >= 0.5
+      for d = 1:dim
+        dist   = abs(Xgb(d) - X(i,d));
+        X(i,d) = dist * exp(b * l) * cos(2 * pi * l) + Xgb(d);
+      end
+    end
+    XB = X(i,:);  XB(XB > ub) = ub;  XB(XB < lb) = lb; 
+    X(i,:) = XB;
+  end
+  for i = 1:N
+    fit(i) = fun(feat,label,(X(i,:) > 0.5),HO);
+    if fit(i) < fitG
+      fitG = fit(i);
+      Xgb  = X(i,:);
+    end
+  end
+  curve(t) = fitG;
+  fprintf('\nIteration %d Best (WOA)= %f',t,curve(t))
+  t = t + 1;
+end
+Pos   = 1:dim;
+Sf    = Pos((Xgb > 0.5) == 1);
+Nf    = length(Sf);
+sFeat = feat(:,Sf); 
+end
+
+
+
+