Regression prediction | MATLAB implements WOA-ELM whale algorithm to optimize extreme learning machine multi-input single-output regression prediction

Regression prediction | MATLAB implements WOA-ELM whale algorithm to optimize extreme learning machine multi-input single-output regression prediction

List of effects

basic introduction

Matlab implements WOA-ELM whale algorithm to optimize extreme learning machine multi-input regression prediction (complete source code and data)
1. Matlab implements WOA-ELM whale algorithm to optimize extreme learning machine multi-input single-output regression prediction (complete source code and data)
2. Multiple inputs 3. Multi-input and single-output regression prediction;
3. Multi-indicator evaluation, evaluation indicators include: R2, MAE, MSE, RMSE, etc., code quality is extremely high;
4. Whale algorithm optimization parameters are: optimization parameters are weights Value and threshold;
5.excel data, easy to replace, operating environment 2018 and above.

programming

• Complete program and data download method 1 (download directly from the resource): MATLAB implements WOA-ELM whale algorithm to optimize extreme learning machine multi-input single-output regression prediction
• Complete program and data download method 2 (subscribe to the "ELM Extreme Learning Machine" column, and at the same time you can read all the content included in the "ELM Extreme Learning Machine" column, and private message me to get the data after subscription): MATLAB implements WOA-ELM whale algorithm to optimize extreme learning Multi-Input Single-Output Regression Forecasting
• Complete program and data download method 3 (subscribe to the "Smart Learning" column, and at the same time get 3 copies of the program included in the "Smart Learning" column, private message me to get the data after subscription): MATLAB implements WOA-ELM whale algorithm to optimize extreme learning machine Multiple input single output regression prediction

%%  计算初始适应度值
fitness = zeros(1, pop);
for i = 1 : pop
   fitness(i) =  fobj(pop_new(i, :));
end

%%  得到全局最优适应度值
[fitness, index]= sort(fitness);
GBestF = fitness(1); 

%%  得到全局最优种群
for i = 1 : pop
    pop_new(i, :) = pop_lsat(index(i), :);
end

GBestX = pop_new(1, :);
X_new  = pop_new;

%%  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

%%  得到最优值
Best_pos = GBestX;
Best_score = curve(end);

References

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718