智能优化特征选择-基于FPA花朵授粉优化的特征选择分类算法KNN分类器（matlab程序）

%[2012]-"Flower pollination algorithm for global optimization" % (9/12/2020) function FPA = jFlowerPollinationAlgorithmSVM(feat,label,opts) % Parameters lb = 0; ub = 1; thres = 0.5; beta = 1.5; % levy component P = 0.8; % switch probability if isfield(opts,'N'), N = opts.N; end if isfield(opts,'P'), P = opts.P; end if isfield(opts,'T'), max_Iter = opts.T; end if isfield(opts,'beta'), beta = opts.beta; end if isfield(opts,'thres'), thres = opts.thres; end % Objective function fun = @jFitnessFunctionSVM; % 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 % Compute fitness fit = zeros(1,N); fitG = inf; for i = 1:N fit(i) =fun(feat,label,(X(i,:) > thres),opts); % Best flower if fit(i) < fitG fitG = fit(i); Xgb = X(i,:); end end % Pre Xnew = zeros(N,dim); curve = zeros(1,max_Iter); curve(1) = fitG; t = 2; % Iterations while t <= max_Iter for i = 1:N % Global pollination if rand() < P % Levy distribution (2) L = jLevyDistribution(beta,dim); for d = 1:dim % Global pollination (1) Xnew(i,d) = X(i,d) + L(d) * (X(i,d) - Xgb(d)); end % Local pollination else % Different flower j, k in same species R = randperm(N); J = R(1); K = R(2); % Epsilon [0 to 1] eps = rand(); for d = 1:dim % Local pollination (3) Xnew(i,d) = X(i,d) + eps * (X(J,d) - X(K,d)); end end % Check boundary XB = Xnew(i,:); XB(XB > ub) = ub; XB(XB < lb) = lb; Xnew(i,:) = XB; end % Fitness for i = 1:N % Compute fitness Fnew = fun(feat,label,(Xnew(i,:) > thres),opts); % Update if there is better solution if Fnew <= fit(i) X(i,:) = Xnew(i,:); fit(i) = Fnew; end % Best flower if fit(i) < fitG Xgb = X(i,:); fitG = fit(i); end end curve(t) = fitG; fprintf('\nIteration %d Best (FPA)= %f',t,curve(t)) t = t + 1; end % Select features Pos = 1:dim; Sf = Pos((Xgb > thres) == 1); sFeat = feat(:,Sf); % Store results FPA.sf = Sf; FPA.ff = sFeat; FPA.nf = length(Sf); FPA.s = curve; FPA.f = feat; FPA.l = label; end %// Levy Flight // function LF = jLevyDistribution(beta,dim) % Sigma nume = gamma(1 + beta) * sin(pi * beta / 2); deno = gamma((1 + beta) / 2) * beta * 2 ^ ((beta - 1) / 2); sigma = (nume / deno) ^ (1 / beta); % Parameter u & v u = randn(1,dim) * sigma; v = randn(1,dim); % Step step = u ./ abs(v) .^ (1 / beta); LF = 0.01 * step; end