分类预测 | Matlab实现SSA-ELM麻雀优化算法优化极限学习机分类预测

分类预测 | Matlab实现SSA-ELM麻雀优化算法优化极限学习机分类预测

分类效果

基本描述

1.MATLAB实现SSA-ELM麻雀优化算法优化极限学习机分类预测（Matlab完整源码和数据)
2.优化参数为权值和阈值;
3.直接替换数据即可使用，保证程序可正常运行。
4.程序语言为matlab，程序可出分类效果图，迭代优化图，混淆矩阵图。
运行环境matlab2018b及以上。
5.代码特点：参数化编程、参数可方便更改、代码编程思路清晰、注释明细。

程序设计

• 完整程序和数据获取方式（资源出直接下载）Matlab实现SSA-ELM麻雀优化算法优化极限学习机分类预测。

%%  优化算法
for i = 1: Max_iter

   BestF = fitness(1);
   R2 = rand(1);

   for j = 1 : PDNumber
      if(R2 < ST)
          X_new(j, :) = pop_new(j, :) .* exp(-j / (rand(1) * Max_iter));
      else
          X_new(j, :) = pop_new(j, :) + randn() * ones(1, dim);
      end     
   end
   
   for j = PDNumber + 1 : pop
        if(j > (pop - PDNumber) / 2 + PDNumber)
          X_new(j, :) = randn() .* exp((pop_new(end, :) - pop_new(j, :)) / j^2);
        else
          A = ones(1, dim);
          for a = 1 : dim
              if(rand() > 0.5)
                A(a) = -1;
              end
          end
          AA = A' / (A * A');     
          X_new(j, :) = pop_new(1, :) + abs(pop_new(j, :) - pop_new(1, :)) .* AA';
       end
   end
   
   Temp = randperm(pop);
   SDchooseIndex = Temp(1 : SDNumber); 
   
   for j = 1 : SDNumber
       if(fitness(SDchooseIndex(j)) > BestF)
           X_new(SDchooseIndex(j), :) = pop_new(1, :) + randn() .* abs(pop_new(SDchooseIndex(j), :) - pop_new(1, :));
       elseif(fitness(SDchooseIndex(j)) == BestF)
           K = 2 * rand() -1;
           X_new(SDchooseIndex(j), :) = pop_new(SDchooseIndex(j), :) + K .* (abs(pop_new(SDchooseIndex(j), :) - ...
               pop_new(end, :)) ./ (fitness(SDchooseIndex(j)) - fitness(end) + 10^-8));
       end
   end

%%  边界控制
   for j = 1 : pop
       for a = 1 : dim
           if(X_new(j, a) > ub(a))
              X_new(j, a) = ub(a);
           end
           if(X_new(j, a) < lb(a))
              X_new(j, a) = lb(a);
           end
       end
   end 

%%  获取适应度值
   for j = 1 : pop
    fitness_new(j) = fobj(X_new(j, :));
   end
   
%%  获取最优种群
   for j = 1 : pop
       if(fitness_new(j) < GBestF)
          GBestF = fitness_new(j);
          GBestX = X_new(j, :);
       end
   end
   
%%  更新种群和适应度值
   pop_new = X_new;
   fitness = fitness_new;

%%  更新种群 
   [fitness, index] = sort(fitness);
   for j = 1 : pop
      pop_new(j, :) = pop_new(index(j), :);
   end

%%  得到优化曲线
   curve(i) = GBestF;
   avcurve(i) = sum(curve) / length(curve);
end

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229