智能优化特征选择-基于ACO蚁群优化的特征选择分类算法KNN和SVM分类器（matlab程序）

%[2009]-"Text feature selection using ant colony optimization" % (9/12/2020) function ACO = jAntColonyOptimizationSVM(feat,label,opts) % Parameters tau = 1; % pheromone value eta = 1; % heuristic desirability alpha = 1; % control pheromone beta = 0.1; % control heuristic rho = 0.2; % pheromone trail decay coefficient if isfield(opts,'N'), N = opts.N; end if isfield(opts,'T'), max_Iter = opts.T; end if isfield(opts,'tau'), tau = opts.tau; end if isfield(opts,'alpha'), alpha = opts.alpha; end if isfield(opts,'beta'), beta = opts.beta; end if isfield(opts,'rho'), rho = opts.rho; end if isfield(opts,'eta'), eta = opts.eta; end % Objective function fun = @jFitnessFunctionSVM; % Number of dimensions dim = size(feat,2); % Initial Tau & Eta tau = tau * ones(dim,dim); eta = eta * ones(dim,dim); % Pre fitG = inf; fit = zeros(1,N); curve = inf; t = 1; % Iterations while t <= max_Iter % Reset ant X = zeros(N,dim); for i = 1:N % Random number of features num_feat = randi([1,dim]); % Ant start with random position X(i,1) = randi([1,dim]); k = []; if num_feat > 1 for d = 2:num_feat % Start with previous tour k = [k(1:end), X(i, d-1)]; % Edge/Probability Selection (2) P = (tau(k(end),:) .^ alpha) .* (eta(k(end),:) .^ beta); % Set selected position = 0 probability (2) P(k) = 0; % Convert probability (2) prob = P ./ sum(P(:)); % Roulette Wheel selection route = jRouletteWheelSelection(prob); % Store selected position to be next tour X(i,d) = route; end end end % Binary X_bin = zeros(N,dim); for i = 1:N % Binary form ind = X(i,:); ind(ind == 0) = []; X_bin(i,ind) = 1; end % Fitness for i = 1:N % Fitness fit(i) = fun(feat,label,X_bin(i,:),opts); % Global update if fit(i) < fitG Xgb = X(i,:); fitG = fit(i); end end %---// [Pheromone update rule on tauK] // tauK = zeros(dim,dim); for i = 1:N % Update Phromones tour = X(i,:); tour(tour == 0) = []; % Number of features len_x = length(tour); tour = [tour(1:end), tour(1)]; for d = 1:len_x % Feature selected on graph x = tour(d); y = tour(d + 1); % Update delta tau k on graph (3) tauK(x,y) = tauK(x,y) + (1 / (1 + fit(i))); end end %---// [Pheromone update rule on tauG] // tauG = zeros(dim,dim); tour = Xgb; tour(tour == 0) = []; % Number of features len_g = length(tour); tour = [tour(1:end), tour(1)]; for d = 1:len_g % Feature selected on graph x = tour(d); y = tour(d + 1); % Update delta tau G on graph tauG(x,y) = 1 / (1 + fitG); end %---// Evaporate pheromone // (4) tau = (1 - rho) * tau + tauK + tauG; % Save curve(t) = fitG; fprintf('\nIteration %d Best (ACO)= %f',t,curve(t)) t = t + 1; end % Select features based on selected index Sf = Xgb; Sf(Sf == 0) = []; sFeat = feat(:,Sf); % Store results ACO.sf = Sf; ACO.ff = sFeat; ACO.nf = length(Sf); ACO.s = curve; ACO.f = feat; ACO.l = label; end %// Roulette Wheel Selection // function Index = jRouletteWheelSelection(prob) % Cummulative summation C = cumsum(prob); % Random one value, most probability value [0~1] P = rand(); % Roulette wheel for i = 1:length(C) if C(i) > P Index = i; break; end end end