【智能优化算法】基于遗传结合粒子群算法求解单目标优化问题matlab代码

function [y, or1, or2, dmse] = predictor(x, dmodel) %PREDICTOR Predictor for y(x) using the given DACE model. % % Call: y = predictor(x, dmodel) % [y, or] = predictor(x, dmodel) % [y, dy, mse] = predictor(x, dmodel) % [y, dy, mse, dmse] = predictor(x, dmodel) % % Input % x : trial design sites with n dimensions. % For mx trial sites x: % If mx = 1, then both a row and a column vector is accepted, % otherwise, x must be an mx*n matrix with the sites stored % rowwise. % dmodel : Struct with DACE model; see DACEFIT % % Output % y : predicted response at x. % or : If mx = 1, then or = gradient vector/Jacobian matrix of predictor % otherwise, or is an vector with mx rows containing the estimated % mean squared error of the predictor % Three or four results are allowed only when mx = 1, % dy : Gradient of predictor; column vector with n elements % mse : Estimated mean squared error of the predictor; % dmse : Gradient vector/Jacobian matrix of mse % hbn@imm.dtu.dk % Last update August 26, 2002 or1 = NaN; or2 = NaN; dmse = NaN; % Default return values if isnan(dmodel.beta) y = NaN; error(’DMODEL has not been found’) end [m n] = size(dmodel.S); % number of design sites and number of dimensions sx = size(x); % number of trial sites and their dimension if min(sx) == 1 & n > 1 % Single trial point nx = max(sx); if nx == n mx = 1; x = x(:).’; end else mx = sx(1); nx = sx(2); end if nx ~= n error(sprintf(’Dimension of trial sites should be %d’,n)) end % Normalize trial sites x = (x - repmat(dmodel.Ssc(1,:),mx,1)) ./ repmat(dmodel.Ssc(2,:),mx,1); q = size(dmodel.Ysc,2); % number of response functions y = zeros(mx,q); % initialize result if mx == 1 % one site only dx = repmat(x,m,1) - dmodel.S; % distances to design sites if nargout > 1 % gradient/Jacobian wanted [f df] = feval(dmodel.regr, x); [r dr] = feval(dmodel.corr, dmodel.theta, dx); % Scaled Jacobian dy = (df * dmodel.beta).’ + dmodel.gamma * dr; % Unscaled Jacobian or1 = dy .* repmat(dmodel.Ysc(2, :)’, 1, nx) ./ repmat(dmodel.Ssc(2,:), q, 1); if q == 1 % Gradient as a column vector or1 = or1’; end if nargout > 2 % MSE wanted rt = dmodel.C r; u = dmodel.Ft.’ * rt - f.’; v = dmodel.G u; or2 = repmat(dmodel.sigma2,mx,1) .* repmat((1 + sum(v.^2) - sum(rt.^2))’,1,q); if nargout > 3 % gradient/Jacobian of MSE wanted % Scaled gradient as a row vector Gv = dmodel.G’ v; g = (dmodel.Ft * Gv - rt)’ * (dmodel.C dr) - (df * Gv)’; % Unscaled Jacobian dmse = repmat(2 * dmodel.sigma2’,1,nx) .* repmat(g ./ dmodel.Ssc(2,:),q,1); if q == 1 % Gradient as a column vector dmse = dmse’; end end end else % predictor only f = feval(dmodel.regr, x); r = feval(dmodel.corr, dmodel.theta, dx); end % Scaled predictor sy = f * dmodel.beta + (dmodel.gamma*r).’; % Predictor y = (dmodel.Ysc(1,:) + dmodel.Ysc(2,:) .* sy)’; else % several trial sites % Get distances to design sites dx = zeros(mx*m,n); kk = 1:m; for k = 1 : mx dx(kk, : ) = repmat(x(k,:),m,1) - dmodel.S; kk = kk + m; end % Get regression function and correlation f = feval(dmodel.regr, x); r = feval(dmodel.corr, dmodel.theta, dx); r = reshape(r, m, mx); % Scaled predictor sy = f * dmodel.beta + (dmodel.gamma * r).’; % Predictor y = repmat(dmodel.Ysc(1,:),mx,1) + repmat(dmodel.Ysc(2,:),mx,1) .* sy; if nargout > 1 % MSE wanted rt = dmodel.C r; u = dmodel.G (dmodel.Ft.’ * rt - f.’); or1 = repmat(dmodel.sigma2,mx,1) .* repmat((1 + colsum(u.^2) - colsum(rt.^2))’,1,q); if nargout > 2 disp(’WARNING from PREDICTOR. Only y and or1=mse are computed’) end end end % of several sites % >>>>>>>>>>>>>>>> Auxiliary function ==================== function s = colsum(x) % Columnwise sum of elements in x if size(x,1) == 1, s = x; else, s = sum(x); end