蝗虫（蚱蜢）算法优化BP神经网络，蝗虫（蚱蜢）算法求解最优路径，蝗虫（算法参数反演，蝗虫算法求解函数最小值（代码完整，数据齐全）

% The Grasshopper Optimization Algorithm function [TargetFitness,TargetPosition,Convergence_curve,Trajectories,fitness_history, position_history]=GOA(N, Max_iter, lb,ub, dim, fobj) % disp('GOA is now estimating the global optimum for your problem....') global Function_name if strcmp(Function_name,'F25') global inputnum global hiddennum global outputnum global net global inputn global outputn global inputn_test global output_test global inputps global outputps global test_simub end flag=0; if size(ub,1)==1 ub=ones(dim,1).*ub; lb=ones(dim,1).*lb; end if (rem(dim,2)~=0) % this algorithm should be run with a even number of variables. This line is to handle odd number of variables dim = dim+1; ub(dim)=100; lb(dim)=100; % ub = [ub,100]; % lb = [lb,-100]; flag=1; end %Initialize the population of grasshoppers GrassHopperPositions=initialization(N,dim,ub,lb); GrassHopperFitness = zeros(1,N); fitness_history=zeros(N,Max_iter); position_history=zeros(N,Max_iter,dim); Convergence_curve=zeros(1,Max_iter); Trajectories=zeros(N,Max_iter); cMax=1; cMin=0.00004; %Calculate the fitness of initial grasshoppers for i=1:size(GrassHopperPositions,1) if flag == 1 GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,1:end-1)); else GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,:)); end fitness_history(i,1)=GrassHopperFitness(1,i); position_history(i,1,:)=GrassHopperPositions(i,:); Trajectories(:,1)=GrassHopperPositions(:,1); end [sorted_fitness,sorted_indexes]=sort(GrassHopperFitness); % Find the best grasshopper (target) in the first population for newindex=1:N Sorted_grasshopper(newindex,:)=GrassHopperPositions(sorted_indexes(newindex),:); end TargetPosition=Sorted_grasshopper(1,:); TargetFitness=sorted_fitness(1); % Main loop l=2; % Start from the second iteration since the first iteration was dedicated to calculating the fitness of antlions while l<Max_iter+1 c=cMax-l*((cMax-cMin)/Max_iter); % Eq. (2.8) in the paper for i=1:size(GrassHopperPositions,1) temp= GrassHopperPositions'; for k=1:2:dim S_i=zeros(2,1); for j=1:N if i~=j Dist=distance(temp(k:k+1,j), temp(k:k+1,i)); % Calculate the distance between two grasshoppers r_ij_vec=(temp(k:k+1,j)-temp(k:k+1,i))/(Dist+eps); % xj-xi/dij in Eq. (2.7) xj_xi=2+rem(Dist,2); % |xjd - xid| in Eq. (2.7) s_ij=((ub(k:k+1) - lb(k:k+1))*c/2)*S_func(xj_xi).*r_ij_vec; % The first part inside the big bracket in Eq. (2.7) S_i=S_i+s_ij; end end S_i_total(k:k+1, :) = S_i; end X_new = c * S_i_total'+ (TargetPosition); % Eq. (2.7) in the paper GrassHopperPositions_temp(i,:)=X_new'; end % GrassHopperPositions GrassHopperPositions=GrassHopperPositions_temp; for i=1:size(GrassHopperPositions,1) % Relocate grasshoppers that go outside the search space Tp=GrassHopperPositions(i,:)>ub';Tm=GrassHopperPositions(i,:)<lb';GrassHopperPositions(i,:)=(GrassHopperPositions(i,:).*(~(Tp+Tm)))+ub'.*Tp+lb'.*Tm; % Calculating the objective values for all grasshoppers if flag == 1 GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,1:end-1)); else GrassHopperFitness(1,i)=fobj(GrassHopperPositions(i,:)); end fitness_history(i,l)=GrassHopperFitness(1,i); position_history(i,l,:)=GrassHopperPositions(i,:); Trajectories(:,l)=GrassHopperPositions(:,1); % Update the target if GrassHopperFitness(1,i)<TargetFitness TargetPosition=GrassHopperPositions(i,:); TargetFitness=GrassHopperFitness(1,i); end end Convergence_curve(l)=TargetFitness; disp(['In iteration #', num2str(l), ' , target''s objective = ', num2str(TargetFitness)]) l = l + 1; end if (flag==1) TargetPosition = TargetPosition(1:dim-1); end if strcmp(Function_name,'F25') x = TargetPosition; 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); net=newff(inputn,outputn,[12],{'logsig','tansig'},'trainlm'); net.iw{1,1}=reshape(w1,hiddennum,inputnum); net.lw{2,1}=reshape(w2,outputnum,hiddennum); net.b{1}=reshape(B1,hiddennum,1); net.b{2}=B2'; %% BP网络训练 %网络进化参数 net.trainParam.epochs=1000; net.trainParam.lr=0.1; %net.trainParam.goal=0.000001; net.trainParam.goal=0.0001; %网络训练 [net,per2]=train(net,inputn,outputn); %% BP网络预测 %数据反归一化 an=sim(net,inputn_test); test_simu=mapminmax('reverse',an,outputps); error=(test_simu-output_test); ob = (test_simu-output_test)./output_test; errorb=(test_simub-output_test); figure plot(output_test(1,:),'r-*') hold on plot(test_simu(1,:),'b-o') hold on plot(test_simub(1,:),'k--^') hold off title('预测值和真实值比对') legend('期望输出','长鼻浣熊算法优化BP','BP') xlabel('样本') grid on figure plot(error(1,:),'b-') hold on plot(errorb(1,:),'k--') hold off ylabel('误差','fontsize',12) xlabel('样本','fontsize',12) legend('长鼻浣熊算法优化BP','BP') grid on end end %% function d = distance(a,b) d=sqrt((a(1)-b(1))^2+(a(2)-b(2))^2); end %% function [X]=initialization(N,dim,up,down) if size(up,1)==1 X=rand(N,dim).*(up-down)+down; end if size(up,1)>1 for i=1:dim high=up(i);low=down(i); X(:,i)=rand(1,N).*(high-low)+low; end end end %% function o=S_func(r) f=0.5; l=1.5; o=f*exp(-r/l)-exp(-r); % Eq. (2.3) in the paper end