智能优化特征选择-基于WOA鲸鱼优化的特征选择分类算法KNN和SVM分类器（matlab程序）

function GFWOA = jGFWhaleOptimizationAlgorithm(feat,label,opts) % Parameters lb = 0; ub = 1; thres = 0.5; b = 1; % constant if isfield(opts,'T'), max_Iter = opts.T; end if isfield(opts,'N'), N = opts.N; end if isfield(opts,'b'), b = opts.b; 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); X1 = zeros(N,dim);%一次反向点 X2 = zeros(N,dim);%二次反向点 X3 = zeros(N,dim);%最终反向点 for i = 1:N for d = 1:dim X(i,d) = lb + (ub - lb) * rand(); end end for i = 1:N for d = 1:dim X1(i,d) = lb + ub - X(i,d); end end for i = 1:N for d = 1:dim X2(i,d) = (lb + ub)/2; end end for i = 1:N for d = 1:dim X3(i,d) = X2(i,d)+(abs(X1(i,d)-X2(i,d)) * rand()); end end % Fitness fit = zeros(1,N); fit1 = zeros(1,N);%保存反向点种群适应度值 fitG = inf; for i = 1:N fit(i) = fun(feat,label,(X(i,:) > thres),opts); fit1(i) = fun(feat,label,(X3(i,:) > thres),opts); if fit1(i) < fit(i) fit(i) = fit1(i); X(i,:) = X3(i,:); end %end % Global best if fit(i) < fitG fitG = fit(i); Xgb = X(i,:); end end % Pre curve = zeros(1,max_Iter); curve(1) = fitG; t = 2; while t <= max_Iter % Define a, linearly decreases from 2 to 0 a = 2 - t * (2 / max_Iter); for i = 1:N % Parameter A (2.3) A = 2 * a * rand() - a; % Paramater C (2.4) C = 2 * rand(); % Parameter p, random number in [0,1] p = rand(); % Parameter l, random number in [-1,1] l = -1 + 2 * rand(); % Whale position update (2.6) if p < 0.5 % {1} Encircling prey if abs(A) < 1 for d = 1:dim % Compute D (2.1) sigma = abs(log(t)/t *(X(i,d)-Xgb(d))); X(i,d) = normrnd(Xgb(d),sigma,1,1)+(rand()*Xgb(d)-rand()*X(i,d)); %Dx = abs(C * Xgb(d) - X(i,d)); % Position update (2.2) %X(i,d) = Xgb(d) - A * Dx; end % {2} Search for prey elseif abs(A) >= 1 for d = 1:dim % Select a random whale k = randi([1,N]); % Compute D (2.7) Dx = abs(C * X(k,d) - X(i,d)); % Position update (2.8) X(i,d) = X(k,d) - A * Dx; end end % {3} Bubble-net attacking elseif p >= 0.5 for d = 1:dim % Distance of whale to prey dist = abs(Xgb(d) - X(i,d)); % Position update (2.5) X(i,d) = dist * exp(b * l) * cos(2 * pi * l) + Xgb(d); end end % Boundary XB = X(i,:); XB(XB > ub) = ub; XB(XB < lb) = lb; X(i,:) = XB; end % Fitness for i = 1:N % Fitness fit(i) = fun(feat,label,(X(i,:) > thres),opts); % Global best if fit(i) < fitG fitG = fit(i); Xgb = X(i,:); end end curve(t) = fitG; fprintf('\nIteration %d Best (GFWOA)= %f',t,curve(t)) t = t + 1; end % Select features Pos = 1:dim; Sf = Pos((Xgb > thres) == 1); sFeat = feat(:,Sf); % Store results GFWOA.sf = Sf; GFWOA.ff = sFeat; GFWOA.nf = length(Sf); GFWOA.s = curve; GFWOA.f = feat; GFWOA.l = label; end