AIA.rar_人工免疫_人工免疫算法_免疫 matlab_免疫 算法_免疫算法

一个基于Matlab的人工免疫算法 %Immune Algorithm based on the immune network model for function f(x1,x2) optimum %copy right SCUT Guangxing Tan 2005.02.18 clear all; %Parameters Size=120; G=200; CodeL=15; E=round(rand(Size,2*CodeL)); %Initial Code %Main Program for k=1:1:G time(k)=k; for s=1:1:Size m=E(s,:); y1=0;y2=0; %Uncoding m1=m(1:1:CodeL); for i=1:1:CodeL y1=y1+m1(i)*2^(i-1); end x1=10.24*y1/65535.0-5.12; m2=m(CodeL+1:1:2*CodeL); for i=1:1:CodeL y2=y2+m2(i)*2^(i-1); end x2=10.24*y2/65535.0-5.12; %f(X1,X2)=(a/(b+(x1*x1+x2*x2)))*(a/(b+(x1*x1+x2*x2)))+(x1*x1+x2*x2)*(x1*x1+x2*x2) %here -5.12=<x1<-2, -5.12=<x2<=5.12, a=3.0, b=0.05; % f(X1,X2)=90+(a/(b+(x1*x1+x2*x2)))*(a/(b+(x1*x1+x2*x2)))+(x1*x1+x2*x2)*(x1*x1+x2*x2) % here -2=<x1<=5.12, -5.12=<x2<=5.12 % f(X)=(-x+3*M_PI-0.5)(1+sinx*sinx) if 1.5M_PI<x<=2M_PI; if x1<-2 F(s)=(3.0/(0.05+(x1*x1+x2*x2)))*(3.0/(0.05+(x1*x1+x2*x2)))+(x1*x1+x2*x2)*(x1*x1+x2*x2); else F(s)=90+(3.0/(0.05+(x1*x1+x2*x2)))*(3.0/(0.05+(x1*x1+x2*x2)))+(x1*x1+x2*x2)*(x1*x1+x2*x2); end end sumf=0; for s=1:1:Size; sumf = sumf+F(s); end favg=sumf./Size; %initiate density x=F./sumf; % This function updates the concentrations of the population antibodies % according to the algorithm update rule: %UpdateConcentrations %Hamming distance for i=1:1:Size for j=1:1:Size H(i,j)=0; for Q=1:1:20 H(i,j)=H(i,j)+E(i,Q).*(1-E(j,Q))+E(j,Q).*(1-E(i,Q)); end affinity(i,j)=F(i)*H(i,j); end end % 亲密度计算完毕 %**************以上是人工免疫的，以下是遗传算法的交叉、突变代码***************** %Euclider distance %for i=1:1:Size; %for j=1:1:Size; %H(i,j)=0; %H(i,j)=sqrt((x1(i)-x1(j)).^2+(x2(i)-x2(j)).^2); %affinity(i,j)=F(i)./(H(i,j)+1); %end %end % This function normalizes the values of the concentrations of the % population. If the concentrations are perceived as percentages of % the total concentration of the system then their sum must be 1.0. % This function recalculates the concentrations so that this rule % is followed. c =0.8; k1 =0.1; k2 =0.3; %x = popv(:,fitness); for i = 1:1:Size xsum = 0.0; for j = 1:1:Size xsum= abs(xsum+(F(j).*affinity(i,j)-k1.*F(j).*affinity(j,i)-k2/(c*Size)).*F(i)); %m(j,i)=popv(i,fitness).*affinity(i,j); %m(i,j)=popv(j,fitness).*affinity(j,i); %n(i,j)= m(j,i)-k1.*m(i,j)-k2/(c*popsize); %sum= sum+n(i,j).*popv(j,fitness); end x=x+x.*c.*xsum; end Fitness=x./sum(x); Ji=1./Fitness; %****** Step 1 : Evaluate BestJ ****** fi=Fitness; %Fitness Function [Oderfi,Indexfi]=sort(fi); %Arranging fi small to bigger Bestfi=Oderfi(Size); %Let Bestfi=max(fi) BestS=E(Indexfi(Size),:); %Let BestS=E(m), m is the Indexfi belong to max(fi) bfi(k)=Bestfi; %****** Step 2 : Select and Reproduct Operation****** fi_sum=sum(fi); fi_Size=(Oderfi/fi_sum)*Size; fi_S=floor(fi_Size); %Selecting Bigger fi value kk=1; for i=1:1:Size for j=1:1:fi_S(i) %Select and Reproduce TempE(kk,:)=E(Indexfi(i),:); kk=kk+1; %kk is used to reproduce end end %************ Step 3 : Crossover Operation ************ pc=0.80; n=ceil(20*rand); for i=1:2:(Size-1) temp=rand; if pc>temp %Crossover Condition for j=n:1:20 TempE(i,j)=E(i+1,j); TempE(i+1,j)=E(i,j); end end end TempE(Size,:)=BestS; E=TempE; %************ Step 4: Mutation Operation ************** %pm=0.001; %pm=0.001-[1:1:Size]*(0.001)/Size; %Bigger fi, smaller Pm %pm=0.0; %No mutation pm=0.1; %Big mutation for i=1:1:Size for j=1:1:2*CodeL temp=rand; if pm>temp %Mutation Condition (varify incident) if TempE(i,j)==0 TempE(i,j)=1; else TempE(i,j)=0; end end end end %Guarantee TempPop(30,:) is the code belong to the best individual(max(fi)) TempE(Size,:)=BestS; E=TempE; M(k)=F(Size); end % Is special for if k Max_Value=F(Size) BestS x1 x2 figure(1); plot(time,M); xlabel('Times');ylabel('Best F'); figure(2); plot(time,bfi); xlabel('times');ylabel('Best Fitness'); 上面的那段程序本身是有些问题的，我后来做了一些改动，由于程序太长，全发出来大家反而会觉得很乱，下面我就列出要点： Anti=[2 3 5;1 6 3;6 2 8]; %假设初始抗体 下面主要列出亲和力的求法以及对抗体进行高变异克隆进行寻优： for ii=1:100 t=0.05; for i=1:3 for j=1:3 Aff(i,j)=1/(1+exp(t*Anti(i,j))); %求出亲和力 end end a=max(max(Aff));b=min(min(Aff)); mld=(a+b)/2; for i=1:3 for j=1:3 if Aff(i,j)<=mld s=exp((b-Aff(i,j))/(a-b+0.1)); Anti(i,j)=Anti(i,j)^(0.5)+s+abs(randn); %高变异克隆算法 end end end end 希望这段程序能够提供一些实现算法的方法，大家互相切磋！