蜣螂优化算法（DBO）优化bp网络

% ----------------------------------------------------------------------------------------------------------- % Dung Beetle Optimizer: (DBO) (demo) % Programmed by Jian-kai Xue % Updated 28 Nov. 2022. % % This is a simple demo version only implemented the basic % idea of the DBO for solving the unconstrained problem. % The details about DBO are illustratred in the following paper. % (To cite this article): % Jiankai Xue & Bo Shen (2022) Dung beetle optimizer: a new meta-heuristic % algorithm for global optimization. The Journal of Supercomputing, DOI: % 10.1007/s11227-022-04959-6 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % qq群：439115722 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc clear %读取数据 load data input output %节点个数 inputnum=2; hiddennum=5; outputnum=1; %训练数据和预测数据 input_train=input(1:1900,:)'; input_test=input(1901:2000,:)'; output_train=output(1:1900)'; output_test=output(1901:2000)'; %选连样本输入输出数据归一化 [inputn,inputps]=mapminmax(input_train); [outputn,outputps]=mapminmax(output_train); %构建网络 net=newff(inputn,outputn,hiddennum); % 参数初始化 dim=21; maxgen=100; % 进化次数 sizepop=30; %种群规模 popmax=5; popmin=-5; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% P_percent = 0.2; % The population size of producers accounts for "P_percent" percent of the total population size pNum = round( sizepop * P_percent ); % The population size of the producers %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i=1:sizepop pop(i,:)=5*rands(1,21); % V(i,:)=rands(1,21); fitness(i)=fun(pop(i,:),inputnum,hiddennum,outputnum,net,inputn,outputn); end XX= pop; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pFit = fitness; [ fMin, bestI ] = min( fitness ); % fMin denotes the global optimum fitness value bestX = pop( bestI, : ); % bestX denotes the global optimum position corresponding to fMin %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 蜣螂优化算法 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for t = 1 : maxgen [ ans, sortIndex ] = sort( pFit );% Sort. [fmax,B]=max( pFit ); worse= pop(B,:); r2=rand(1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : pNum if(r2<0.9) r1=rand(1); a=rand(1,1); if (a>0.1) a=1; else a=-1; end pop( i , : ) = pop( i , :)+0.3*abs(pop(i , : )-worse)+a*0.1*(XX( i , :)); % Equation (1) else aaa= randperm(180,1); if ( aaa==0 ||aaa==90 ||aaa==180 ) pop( i , : ) = pop( i , :); end theta= aaa*pi/180; pop( i , : ) = pop( i , :)+tan(theta).*abs( pop(i , : )-XX( i , :)); % Equation (2) end fitness( i )=fun(pop(i ,:),inputnum,hiddennum,outputnum,net,inputn,outputn); end [ fMMin, bestII ] = min( fitness ); bestXX = pop( bestII, : ); R=1-t/maxgen; % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew1 = bestXX.*(1-R); Xnew2 =bestXX.*(1+R); %%% Equation (3) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew11 = bestX.*(1-R); Xnew22 =bestX.*(1+R); %%% Equation (5) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = ( pNum + 1 ) :12 % Equation (4) pop( i, : )=bestXX+((rand(1,dim)).*(pop( i , : )-Xnew1)+(rand(1,dim)).*(pop( i , : )-Xnew2)); fitness( i )=fun(pop(i ,:),inputnum,hiddennum,outputnum,net,inputn,outputn); end for i = 13: 19 % Equation (6) pop( i, : )=pop( i , : )+((randn(1)).*(pop( i , : )-Xnew11)+((rand(1,dim)).*(pop( i , : )-Xnew22))); fitness( i )=fun(pop(i ,:),inputnum,hiddennum,outputnum,net,inputn,outputn); end for j = 20 : sizepop % Equation (7) pop( j,: )=bestX+randn(1,dim).*((abs(( pop(j,: )-bestXX)))+(abs(( pop(j,: )-bestX))))./2; fitness( j )=fun(pop(j ,:),inputnum,hiddennum,outputnum,net,inputn,outputn); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% XX=pop; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : sizepop if ( fitness( i ) < pFit( i ) ) pFit( i ) = fitness( i ); pop(i,:) = pop(i,:); end if( pFit( i ) < fMin ) fMin= pFit( i ); bestX =pop( i, : ); end end yy(t)=fMin; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 迭代寻优 x=bestX %% 结果分析 plot(yy) title(['适应度曲线 ' '终止代数＝' num2str(maxgen)]); xlabel('进化代数');ylabel('适应度'); %% 把最优初始阀值权值赋予网络预测 % %用蜣螂优化算法优化的BP网络进行值预测 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.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; %% 训练 %网络进化参数 net.trainParam.epochs=100; net.trainParam.lr=0.1; net.trainParam.goal=0.00001; %网络训练 [net,tr]=train(net,inputn,outputn); %%预测 %数据归一化 inputn_test=mapminmax('apply',input_test,inputps); an=sim(net,inputn_test); test_simu=mapminmax('reverse',an,outputps); error=test_simu-output_test; figure(2) plot(error) title('仿真预测误差','fontsize',12); xlabel('仿真次数','fontsize',12);ylabel('误差百分值','fontsize',12);