1 简介

2 部分代码

function [sFeat,Sf,Nf,curve] = jSSA2(feat,label,N,max_Iter,HO)

lb    = 0;

ub    = 1;

thres = 0.5;

fun = @jFitnessFunction;

dim = size(feat,2); 

X   = zeros(N,dim); 

for i = 1:N

for d = 1:dim

    X(i,d) = lb + (ub - lb) * rand();

  end

end

% Pre

fit   = zeros(1,N);

fitF  = inf;

curve = inf; 

t = 1;  

%---Iteration start----------------------------------------------------

while t <= max_Iter

  for i = 1:N

    fit(i) = fun(feat,label,(X(i,:) > thres),HO);

    if fit(i) < fitF

      Xf   = X(i,:);

      fitF = fit(i); 

    end

  end

  % Additional sort in the first iteration to improve the 

  % initial behavior by divide salps into leader and followers

  if t == 1

    [fit, idx] = sort(fit,'ascend');

    X          = X(idx,:); 

  end

c1 = 2 * exp(-(4 * t / max_Iter) ^ 2);

for i = 1:N

    if i == 1

      for d = 1:dim

        c2 = rand(); 

        c3 = rand();

        if c3 >= 0.5 

          X(i,d) = Xf(d) + c1 * ((ub - lb) * c2 + lb);

        else

          X(i,d) = Xf(d) - c1 * ((ub - lb) * c2 + lb);

        end

      end

    else

      for d = 1:dim

        X(i,d) = (X(i,d) + X(i-1,d)) / 2;

      end

    end

    XB = X(i,:);  XB(XB > ub) = ub;  XB(XB < lb) = lb;

    X(i,:) = XB;

  end

  curve(t) = fitF;

  fprintf('\nIteration %d Best (SSA)= %f',t,curve(t))

  t = t + 1;

end

Pos   = 1:dim;

Sf    = Pos((Xf > thres) == 1);

Nf    = length(Sf);

sFeat = feat(:,Sf); 

end

3 仿真结果

 4 参考文献

[1]范千、陈振健、夏樟华. 一种基于折射反向学习机制与自适应控制因子的改进樽海鞘群算法[J]. 哈尔滨工业大学学报, 2020, 52(10):9.

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