长鼻浣熊算法优化BP神经网络的回归分析，长鼻浣熊算法的函数最小值求解，长鼻浣熊算法的参数反演，长鼻浣熊算法路径优化（代码完整）

function [lb,ub,dim,fobj] = Get_Functions_details(F) switch F case 'F1' fobj = @F1; lb=-100; ub=100; dim=30; case 'F2' fobj = @F2; lb=-10; ub=10; dim=30; case 'F3' fobj = @F3; lb=-100; ub=100; dim=30; case 'F4' fobj = @F4; lb=-100; ub=100; dim=30; case 'F5' fobj = @F5; lb=-30; ub=30; dim=30; case 'F6' fobj = @F6; lb=-100; ub=100; dim=30; case 'F7' fobj = @F7; lb=-1.28; ub=1.28; dim=30; case 'F8' fobj = @F8; lb=-500; ub=500; dim=30; case 'F9' fobj = @F9; lb=-5.12; ub=5.12; dim=30; case 'F10' fobj = @F10; lb=-32; ub=32; dim=30; case 'F11' fobj = @F11; lb=-600; ub=600; dim=30; case 'F12' fobj = @F12; lb=-50; ub=50; dim=30; case 'F13' fobj = @F13; lb=-50; ub=50; dim=30; case 'F14' fobj = @F14; lb=-65.536; ub=65.536; dim=2; case 'F15' fobj = @F15; lb=-5; ub=5; dim=4; case 'F16' fobj = @F16; lb=-5; ub=5; dim=2; case 'F17' fobj = @F17; lb=[-5,0]; ub=[10,15]; dim=2; case 'F18' fobj = @F18; lb=-2; ub=2; dim=2; case 'F19' fobj = @F19; lb=0; ub=1; dim=3; case 'F20' fobj = @F20; lb=0; ub=1; dim=6; case 'F21' fobj = @F21; lb=0; ub=10; dim=4; case 'F22' fobj = @F22; lb=0; ub=10; dim=4; case 'F23' fobj = @F23; lb=0; ub=10; dim=4; case 'F24' fobj = @F24; lb=1; ub=20; dim=10; case 'F25' fobj = @F25; lb=-1; ub=1; inputnum=20; hiddennum=12; outputnum=1; dim=inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum; end end % F24 function o = F24(x) y = zeros(1,19); gs = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0; 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0; 0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0; 0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0; 0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]; ij = [3,5;3,16;3,17;3,18;4,5;4,16;4,17;4,18;5,8;5,9;5,10;5,18;6,16;6,17;6,18;10,10;10,11;11,10;11,11;13,18;14,18;15,5;15,12;16,5;16,6;16,7;16,8;16,12]; x2 = sort(x(6:10)); x2(1) = 1; x2(10)=20; for ii = 1:4 x(1)=1; x(5)=20; p2 = x(ii:ii+1); p1 = x2(ii:ii+1); pan =0; for jj = 1:length(ij) p0 = ij(jj,:); d = abs(det(([p2-p1;p0-p1])))/norm(p2-p1); if d<0.8 pan = pan+1; break end end if pan==0 y(ii) = sqrt((x(ii+1)-x(ii))^2+1); else y(ii) = sqrt((x(ii+1)-x(ii))^2+1)*10; end end o = sum(y); end function error =F25(x) %该函数用来计算适应度值 %x input 个体 %inputnum input 输入层节点数 %outputnum input 隐含层节点数 %net input 网络 %inputn input 训练输入数据 %outputn input 训练输出数据 %error output 个体适应度值 global inputnum global hiddennum global outputnum global net global inputn global outputn global inputn_test global outputn_test %提取 w1=x(1:inputnum*hiddennum); B1=x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum); w2=x(inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum); B2=x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum); net1 = net; %net=newff(inputn,outputn,hiddennum); %网络进化参数 net1.trainParam.epochs=10; net1.trainParam.lr=0.1; net1.trainParam.goal=0.00001; %net.trainParam.show=200; net1.trainParam.showWindow=false; %网络权值赋值 net1.iw{1,1}=reshape(w1,hiddennum,inputnum); %net.iw{1,1}=reshape(w1,hiddennum,1); net1.lw{2,1}=reshape(w2,outputnum,hiddennum); net1.b{1}=reshape(B1,hiddennum,1); net1.b{2}=reshape(B2,outputnum,1); %网络训练 % net1=train(net1,inputn,outputn); an=sim(net1,inputn); error=sum(abs(an(:)-outputn(:))); end function o=Ufun(x,a,k,m) o=k.*((x-a).^m).*(x>a)+k.*((-x-a).^m).*(x<(-a)); end