layers0 = [ ... % 输入特征 sequenceInputLayer([numFeatures,1,1],'name','input') %输入层设置 sequenceFoldingLayer('name','fold') %使用序列折叠层对图像序列的时间步长进行独立的卷积运算。 % CNN特征提取 convolution2dLayer([3,1],16,'Stride',[1,1],'name','conv1') %添加卷积层，64，1表示过滤器大小，10过滤器个数，Stride是垂直和水平过滤的步长 batchNormalizationLayer('name','batchnorm1') % BN层，用于加速训练过程，防止梯度消失或梯度爆炸 reluLayer('name','relu1') % ReLU激活层，用于保持输出的非线性性及修正梯度的问题 % 池化层 maxPooling2dLayer([2,1],'Stride',2,'Padding','same','name','maxpool') % 第一层池化层，包括3x3大小的池化窗口，步长为1，same填充方式 % 展开层 sequenceUnfoldingLayer('name','unfold') %独立的卷积运行结束后，要将序列恢复 %平滑层 flattenLayer('name','flatten') selfAttentionLayer(2,2) %创建2个头，2个键和查询通道的自注意力层 dropoutLayer(0.1,'name','dropout_1') % Dropout层，以概率为0.2丢弃输入 fullyConnectedLayer(1,'name','fullconnect') % 全连接层设置（影响输出维度）（cell层出来的输出层） % regressionLayer('Name','output') ]; lgraph0 = layerGraph(layers0); lgraph0 = connectLayers(lgraph0,'fold/miniBatchSize','unfold/miniBatchSize'); pNum = round( pop * P_percent ); % The population size of the producers for t=1:MaxIt %% 1- select_space [pop BestSol s1(t)]=select_space(fobj,pop,nPop,BestSol,low,high,dim); %% 2- search in space [pop BestSol s2(t)]=search_space(fobj,pop,BestSol,nPop,low,high); %% 3- swoop [pop BestSol s3(t)]=swoop(fobj,pop,BestSol,nPop,low,high); Convergence_curve(t)=BestSol.cost; disp(num2str([t BestSol.cost])) ed=cputime; timep=ed-st; end function [pop BestSol s1]=select_space(fobj,pop,npop,BestSol,low,high,dim) Mean=mean(pop.pos); % Empty Structure for Individuals empty_individual.pos = []; empty_individual.cost = []; lm= 2; s1=0; for i=1:npop newsol=empty_individual; newsol.pos= BestSol.pos+ lm*rand(1,dim).*(Mean - pop.pos(i,:)); newsol.pos = max(newsol.pos, low); newsol.pos = min(newsol.pos, high); newsol.cost=fobj(newsol.pos); if newsol.cost<pop.cost(i) pop.pos(i,:) = newsol.pos; pop.cost(i)= newsol.cost; s1=s1+1; if pop.cost(i) < BestSol.cost BestSol.pos= pop.pos(i,:); BestSol.cost=pop.cost(i); end end end