devec3.m

模式识别工具箱。非常丰富的底层函数和常见的统计识别工具

        ibest   = i;                 % save its location
        bestval = val(i);
    end
end
bestmemit = pop(ibest,:);         % best member of current iteration
bestvalit = bestval;              % best value of current iteration

bestmem = bestmemit;              % best member ever

%------DE-Minimization---------------------------------------------
%------popold is the population which has to compete. It is--------
%------static through one iteration. pop is the newly--------------
%------emerging population.----------------------------------------

pm1 = zeros(NP,D);              % initialize population matrix 1
pm2 = zeros(NP,D);              % initialize population matrix 2
pm3 = zeros(NP,D);              % initialize population matrix 3
pm4 = zeros(NP,D);              % initialize population matrix 4
pm5 = zeros(NP,D);              % initialize population matrix 5
bm  = zeros(NP,D);              % initialize bestmember  matrix
ui  = zeros(NP,D);              % intermediate population of perturbed vectors
mui = zeros(NP,D);              % mask for intermediate population
mpo = zeros(NP,D);              % mask for old population
rot = (0:1:NP-1);               % rotating index array (size NP)
rotd= (0:1:D-1);                % rotating index array (size D)
rt  = zeros(NP);                % another rotating index array
rtd = zeros(D);                 % rotating index array for exponential crossover
a1  = zeros(NP);                % index array
a2  = zeros(NP);                % index array
a3  = zeros(NP);                % index array
a4  = zeros(NP);                % index array
a5  = zeros(NP);                % index array
ind = zeros(4);

iter = 1;
while (bestval > VTR & iter < itermax)
    popold = pop;                   % save the old population

    ind = randperm(4);              % index pointer array

    a1  = randperm(NP);             % shuffle locations of vectors
    rt = rem(rot+ind(1),NP);        % rotate indices by ind(1) positions
    a2  = a1(rt+1);                 % rotate vector locations
    rt = rem(rot+ind(2),NP);
    a3  = a2(rt+1);
    rt = rem(rot+ind(3),NP);
    a4  = a3(rt+1);
    rt = rem(rot+ind(4),NP);
    a5  = a4(rt+1);

    pm1 = popold(a1,:);             % shuffled population 1
    pm2 = popold(a2,:);             % shuffled population 2
    pm3 = popold(a3,:);             % shuffled population 3
    pm4 = popold(a4,:);             % shuffled population 4
    pm5 = popold(a5,:);             % shuffled population 5

    for i=1:NP                      % population filled with the best member
        bm(i,:) = bestmemit;          % of the last iteration
    end

    mui = rand(NP,D) < CR;          % all random numbers < CR are 1, 0 otherwise

    if (strategy > 5)
        st = strategy-5;		  % binomial crossover
    else
        st = strategy;		  % exponential crossover
        mui=sort(mui');	          % transpose, collect 1's in each column
        for i=1:NP
            n=floor(rand*D);
            if n > 0
                rtd = rem(rotd+n,D);
                mui(:,i) = mui(rtd+1,i); %rotate column i by n
            end
        end
        mui = mui';			  % transpose back
    end
    mpo = mui < 0.5;                % inverse mask to mui

    if (st == 1)                      % DE/best/1
        ui = bm + F*(pm1 - pm2);        % differential variation
        ui = popold.*mpo + ui.*mui;     % crossover
    elseif (st == 2)                  % DE/rand/1
        ui = pm3 + F*(pm1 - pm2);       % differential variation
        ui = popold.*mpo + ui.*mui;     % crossover
    elseif (st == 3)                  % DE/rand-to-best/1
        ui = popold + F*(bm-popold) + F*(pm1 - pm2);
        ui = popold.*mpo + ui.*mui;     % crossover
    elseif (st == 4)                  % DE/best/2
        ui = bm + F*(pm1 - pm2 + pm3 - pm4);  % differential variation
        ui = popold.*mpo + ui.*mui;           % crossover
    elseif (st == 5)                  % DE/rand/2
        ui = pm5 + F*(pm1 - pm2 + pm3 - pm4);  % differential variation
        ui = popold.*mpo + ui.*mui;            % crossover
    end


    for i=1:D
        tl = find(ui(:,i) < XVmin(i));
        tu = find(ui(:,i) > XVmax(i));
        ui(tl,i) = (XVmin(i) + pop(tl,i))/2;
        ui(tu,i) = (XVmax(i) + pop(tu,i))/2;
    end;


%     for i=1:length(a4)/2
%         k1  = a4((i-1) * 2 + 1);
%         k2  = a4(i * 2);
%         e1 = norm(pop(k1,:) - ui(k1,:)) + norm(pop(k2,:) - ui(k2,:));
%         e2 = norm(pop(k1,:) - ui(k2,:)) + norm(pop(k2,:) - ui(k1,:));
%         if (e1 < e2)
%             tempval = feval(fname,ui(k1,:),y);   % check cost of competitor
%             nfeval  = nfeval + 1;
%             if (tempval <= val(k1))  % if competitor is better than value in "cost array"
%                 pop(k1,:) = ui(k1,:);  % replace old vector with new one (for new iteration)
%                 val(k1)   = tempval;  % save value in "cost array"
% 
%                 %----we update bestval only in case of success to save time-----------
%                 if (tempval < bestval)     % if competitor better than the best one ever
%                     bestval = tempval;      % new best value
%                     bestmem = ui(k1,:);      % new best parameter vector ever
%                 end
%             end
% 
%             tempval = feval(fname,ui(k2,:),y);   % check cost of competitor
%             nfeval  = nfeval + 1;
%             if (tempval <= val(k2))  % if competitor is better than value in "cost array"
%                 pop(k2,:) = ui(k2,:);  % replace old vector with new one (for new iteration)
%                 val(k2)   = tempval;  % save value in "cost array"
% 
%                 %----we update bestval only in case of success to save time-----------
%                 if (tempval < bestval)     % if competitor better than the best one ever
%                     bestval = tempval;      % new best value
%                     bestmem = ui(k2,:);      % new best parameter vector ever
%                 end
%             end
%         else
%             tempval = feval(fname,ui(k1,:),y);   % check cost of competitor
%             nfeval  = nfeval + 1;
%             if (tempval <= val(k2))  % if competitor is better than value in "cost array"
%                 pop(k2,:) = ui(k1,:);  % replace old vector with new one (for new iteration)
%                 val(k2)   = tempval;  % save value in "cost array"
% 
%                 %----we update bestval only in case of success to save time-----------
%                 if (tempval < bestval)     % if competitor better than the best one ever
%                     bestval = tempval;      % new best value
%                     bestmem = ui(k1,:);      % new best parameter vector ever
%                 end
%             end
% 
%             tempval = feval(fname,ui(k2,:),y);   % check cost of competitor
%             nfeval  = nfeval + 1;
%             if (tempval <= val(k1))  % if competitor is better than value in "cost array"
%                 pop(k1,:) = ui(k2,:);  % replace old vector with new one (for new iteration)
%                 val(k1)   = tempval;  % save value in "cost array"
% 
%                 %----we update bestval only in case of success to save time-----------
%                 if (tempval < bestval)     % if competitor better than the best one ever
%                     bestval = tempval;      % new best value
%                     bestmem = ui(k2,:);      % new best parameter vector ever
%                 end
%             end
%         end;
%     end;
    
    
    %-----Select which vectors are allowed to enter the new population------------
  for i=1:NP
    tempval = feval(fname,ui(i,:),y);   % check cost of competitor
%       tempval= feval(fname, ui(i,:), ode_ind, m, u, y, x, xmask,fs,fValue);
    nfeval  = nfeval + 1;
    if (tempval <= val(i))  % if competitor is better than value in "cost array"
       pop(i,:) = ui(i,:);  % replace old vector with new one (for new iteration)
       val(i)   = tempval;  % save value in "cost array"

       %----we update bestval only in case of success to save time-----------
       if (tempval < bestval)     % if competitor better than the best one ever
          bestval = tempval;      % new best value
          bestmem = ui(i,:);      % new best parameter vector ever
       end
    end
  end %---end for imember=1:NP
  
    bestmemit = bestmem;       % freeze the best member of this iteration for the coming
    % iteration. This is needed for some of the strategies.

    %----Output section----------------------------------------------------------

    if (refresh > 0)
        if (rem(iter,refresh) == 0)
            fprintf(1,'Iteration: %d,  Best: %f,  F: %f,  CR: %f,  NP: %d\n',iter,bestval,F,CR,NP);
            for n=1:D
                fprintf(1,'best(%d) = %f\n',n,bestmem(n));
            end
        end
    end

    iter = iter + 1;
    if (FunCount >= 8e5)
        return;
    end;
end %---end while ((iter < itermax) ...