【BP预测】基于Sine混沌映射优化麻雀算法改进BP神经网络实现数据预测

最新推荐文章于 2024-03-28 22:58:40 发布

原创最新推荐文章于 2024-03-28 22:58:40 发布 · 610 阅读

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该博客介绍了 SSA（Swarm Intelligence-based Salp Swarming Algorithm）算法在解决优化问题中的实现过程。首先，详细说明了算法的基本原理和初始化步骤，然后展示了算法的主要代码段，包括迭代过程中的位置更新和适应度计算。在仿真结果部分，可能提到了算法的收敛性和性能比较。最后，引用了一篇关于使用BP神经网络预测宁夏水资源需求量的文章，暗示了SSA算法在实际问题中的应用潜力。

1 模型简介见这里

2 部分代码



function [FoodFitness,FoodPosition,Convergence_curve]=SSA(N,Max_iter,lb,ub,dim,fobj)
if size(ub,1)==1
    ub=ones(dim,1)*ub;
    lb=ones(dim,1)*lb;
end
Convergence_curve = zeros(1,Max_iter);
%Initialize the positions of salps
SalpPositions=initialization(N,dim,ub,lb);
FoodPosition=zeros(1,dim);
FoodFitness=inf;
%calculate the fitness of initial salps
for i=1:size(SalpPositions,1)
    SalpFitness(1,i)=fobj(SalpPositions(i,:));
end
[sorted_salps_fitness,sorted_indexes]=sort(SalpFitness);
for newindex=1:N
    Sorted_salps(newindex,:)=SalpPositions(sorted_indexes(newindex),:);
end
FoodPosition=Sorted_salps(1,:);
FoodFitness=sorted_salps_fitness(1);
%Main loop
l=2; % start from the second iteration since the first iteration was dedicated to calculating the fitness of salps
while l<Max_iter+1
    c1 = 2*exp(-(4*l/Max_iter)^2); % Eq. (3.2) in the paper
    for i=1:size(SalpPositions,1)
        SalpPositions= SalpPositions';
        if i<=N/2
            for j=1:1:dim
                c2=rand();
                c3=rand();
                %%%%%%%%%%%%% % Eq. (3.1) in the paper %%%%%%%%%%%%%%
                if c3<0.5 
                    SalpPositions(j,i)=FoodPosition(j)+c1*((ub(j)-lb(j))*c2+lb(j));
                else
                    SalpPositions(j,i)=FoodPosition(j)-c1*((ub(j)-lb(j))*c2+lb(j));
                end
                %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
            end
        elseif i>N/2 && i<N+1
            point1=SalpPositions(:,i-1);
            point2=SalpPositions(:,i);
            SalpPositions(:,i)=(point2+point1)/2; % % Eq. (3.4) in the paper
        end
        SalpPositions= SalpPositions';
    end
    for i=1:size(SalpPositions,1)
        Tp=SalpPositions(i,:)>ub';Tm=SalpPositions(i,:)<lb';SalpPositions(i,:)=(SalpPositions(i,:).*(~(Tp+Tm)))+ub'.*Tp+lb'.*Tm;
        SalpFitness(1,i)=fobj(SalpPositions(i,:));
        if SalpFitness(1,i)<FoodFitness
            FoodPosition=SalpPositions(i,:);
            FoodFitness=SalpFitness(1,i); 
        end
    end
    Convergence_curve(l)=FoodFitness;
    l = l + 1;
end