智能优化特征选择-基于MBO帝王蝴蝶优化的特征选择分类算法（matlab程序）

%[2015]-"Monarch butterfly optimization" % (9/12/2020) function MBO = jMonarchButterflyOptimizationSVM(feat,label,opts) % Parameters lb = 0; ub = 1; thres = 0.5; peri = 1.2; % migration period p = 5/12; % ratio Smax = 1; % maximum step BAR = 5/12; % butterfly adjusting rate num_land1 = 4; % number of butterflies in land 1 beta = 1.5; % levy component if isfield(opts,'T'), max_Iter = opts.T; end if isfield(opts,'N'), N = opts.N; end if isfield(opts,'peri'), peri = opts.peri; end if isfield(opts,'p'), p = opts.p; end if isfield(opts,'Smax'), Smax = opts.Smax; end if isfield(opts,'BAR'), BAR = opts.BAR; end if isfield(opts,'beta'), beta = opts.beta; end if isfield(opts,'N1'), num_land1 = opts.N1; 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 % Fitness fit = zeros(1,N); fitG = inf; for i = 1:N fit(i) = fun(feat,label,(X(i,:) > thres),opts); % Global best update if fit(i) < fitG fitG = fit(i); Xgb = X(i,:); end end % Pre Xnew = zeros(N,dim); Fnew = zeros(1,N); curve = zeros(1,max_Iter); curve(1) = fitG; t = 2; % Iterations while t <= max_Iter % Sort butterfly [fit, idx] = sort(fit,'ascend'); X = X(idx,:); % Weight factor (8) alpha = Smax / (t ^ 2); % {1} First land: Migration operation for i = 1:num_land1 for d = 1:dim % Random number (2) r = rand() * peri; if r <= p % Random select a butterfly in land 1 r1 = randi([1,num_land1]); % Update position (1) Xnew(i,d) = X(r1,d); else % Random select a butterfly in land 2 r2 = randi([num_land1 + 1, N]); % Update position (3) Xnew(i,d) = X(r2,d); end end % Boundary XB = Xnew(i,:); XB(XB > ub) = ub; XB(XB < lb) = lb; Xnew(i,:) = XB; end % {2} Second land: Butterly adjusting operation for i = num_land1 + 1 : N % Levy distribution (7) dx = jLevyDistribution(beta,dim); for d = 1:dim if rand() <= p % Position update (4) Xnew(i,d) = Xgb(d); else % Random select a butterfly in land 2 r3 = randi([num_land1 + 1, N]); % Update position (5) Xnew(i,d) = X(r3,d); % Butterfly adjusting (6) if rand () > BAR Xnew(i,d) = Xnew(i,d) + alpha * (dx(d) - 0.5); end end end % Boundary XB = Xnew(i,:); XB(XB > ub) = ub; XB(XB < lb) = lb; Xnew(i,:) = XB; end % {3} Combine population for i = 1:N % Fitness Fnew(i) = fun(feat,label,(Xnew(i,:) > thres),opts); % Global best update if Fnew(i) < fitG fitG = Fnew(i); Xgb = Xnew(i,:); end end % Merge & Select best N solutions XX = [X; Xnew]; FF = [fit, Fnew]; [FF, idx] = sort(FF,'ascend'); X = XX(idx(1:N),:); fit = FF(1:N); % Save curve(t) = fitG; fprintf('\nIteration %d Best (MBO)= %f',t,curve(t)) t = t + 1; end % Select features Pos = 1:dim; Sf = Pos((Xgb > thres) == 1); sFeat = feat(:,Sf); % Store results MBO.sf = Sf; MBO.ff = sFeat; MBO.nf = length(Sf); MBO.s = curve; MBO.f = feat; MBO.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 = step; end