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⛳️座右铭：行百里者，半于九十。

目录

​​💥1 概述​​

​​📚2 运行结果​​

​​🎉3 参考文献​​

​​🌈4 Matlab代码实现​​


💥1 概述

本文程序用于特征选择的二元粒子群优化（BPSO），可以选择潜在特征以提高分类精度。并讲解如何使用具有分类错误率（由 KNN 计算）的 BPSO 作为使用基准数据集的特征选择问题的适应度函数。

📚2 运行结果

【改进粒子群】基于特征选择的二元粒子群优化算法（Matlab代码实现）_算法

部分代码：

function [sFeat,Sf,Nf,curve] = jBPSO(feat,label,N,max_Iter,c1,c2,HO)
 Wmax = 0.9;
 Wmin = 0.4; 
 Vmax = 6;fun = @jFitnessFunction; 
 dim = size(feat,2); 
 X   = zeros(N,dim);
 V   = zeros(N,dim); 
 for i = 1:N
   for d = 1:dim
     if rand() > 0.5
       X(i,d) = 1;
     end
   end
 end
 fit  = zeros(1,N); 
 fitG = inf;
 for i = 1:N
   fit(i) = fun(feat,label,X(i,:),HO);
   if fit(i) < fitG
     Xgb  = X(i,:);
     fitG = fit(i);
   end
 end
 Xpb  = X; 
 fitP = fit; curve = inf; 
 t = 1; 
 %---Iterations start-------------------------------------------------
 while t <= max_Iter
   w = Wmax - (Wmax - Wmin) * (t / max_Iter);
   for i = 1:N
     for d = 1:dim
       r1    = rand();
       r2    = rand();
       VB    = V(i,d) * w + c1 * r1 * (Xpb(i,d) - X(i,d)) + ...
               c2 * r2 * (Xgb(d) - X(i,d)); 
       VB(VB > Vmax) = Vmax;  VB(VB < -Vmax) = -Vmax; 
       V(i,d) = VB; 
       TF     = 1 / (1 + exp(-V(i,d)));
       if TF > rand()
         X(i,d) = 1;
       else
         X(i,d) = 0;
       end
     end
     fit(i) = fun(feat,label,X(i,:),HO);
     if fit(i) < fitP(i)
       Xpb(i,:) = X(i,:);
       fitP(i)  = fit(i);
     end
     if fitP(i) < fitG
       Xgb  = Xpb(i,:);
       fitG = fitP(i);
     end
   end
   curve(t) = fitG; 
   fprintf('\nIteration %d Best (BSPO)= %f',t,curve(t))
   t = t + 1;
 end
 Pos   = 1:dim;
 Sf    = Pos(Xgb == 1);
 Nf    = length(Sf);
 sFeat = feat(:,Sf); 
 end

🎉3 参考文献

[1]Too, Jingwei, et al. “A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection.” Informatics, vol. 6, no. 2, MDPI AG, May 2019, p. 21, doi:10.3390/informatics6020021.

[2]Too, Jingwei, et al. “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.” Computation, vol. 7, no. 1, MDPI AG, Feb. 2019, p. 12, doi:10.3390/computation7010012.

​​🌈​​4 Matlab代码实现