1 简介
2 部分代码
function [bestY,bestX,recording]=AFO2(x,y,option,data)%% Input% x----positions of initialized populaiton% y----fitnesses of initialized populaiton% option-----parameters set of the algorithm% data------Pre-defined parameters% This parameter is used for solving complex problems is passing case data%% outPut% bestY ----fitness of best individual% bestX ----position of best individual% recording ---- somme data was recorded in this variable%% initializationpe=option.pe;L=option.L;gap0=option.gap0;gap=gap0;dim=option.dim;maxIteration=option.maxIteration;recording.bestFit=zeros(maxIteration 1,1);recording.meanFit=zeros(maxIteration 1,1);numAgent=option.numAgent;At=randn(numAgent,dim);w2=option.w2; %weight of Moving strategy IIIw4=option.w4;%weight of Moving strategy IIIw5=option.w5;%weight of Moving strategy IIIpe=option.pe; % rate to judge Premature convergencegapMin=option.gapMin;dec=option.dec;ub=option.ub;lb=option.lb;v_lb=option.v_lb;v_ub=option.v_ub;fobj=option.fobj;count=1;%% center of population[y_c,position]=min(y);x_c=x(position(1)；At_c=At(position(1)；%% memory of populationy_m=y;x_m=x;%% update recordingrecording.bestFit(1)=y_c;recording.meanFit(1)=mean(y_m);%% main loopiter=1;while iter<=maxIteration%Dmp(['AFO,iter:',num2str(iter),',minFit:',num2str(y_c)])%% Moving Strategy I for center of populationif rem(iter, gap)==0 && dim<option.numAgentc0=exp(-30*(iter-gap0)/maxIteration); % EQ.2-11Dx=ones(1,dim);Dx=c0*Dx/norm(Dx)*norm(v_ub-v_lb)/2; %EQ.2-12 % △xDx1=-Dx; %-△x% △xfor j=1:dimtempX(j,:)=x_c;tempX(j,j)=x_c(1,j) Dx(j);if tempX(j,j)>ub(j)tempX(j,j)=ub(j);Dx(1,j)=tempX(j,j)-x_c(1,j);endif tempX(j,j)<lb(j)tempX(j,j)=lb(j);Dx(1,j)=tempX(j,j)-x_c(1,j);endtempY(j,:)=fobj(tempX(j,:),option,data);if tempY(j)*y_c<0g0(1,j)=(log(tempY(j))-log(y_c))./Dx(j); %EQ.2-18elsetemp=[tempY(j),y_c];temp=temp min(temp) eps;g0(1,j)=(log(temp(1))-log(temp(2)))./Dx(j);endg0(isnan(g0))=0;endG0=-g0(1,:)*norm(v_ub-v_lb)/2/norm(g(1)； % part of Eq 2-18G0(1,G0(1,:)>v_ub)=G0(1,G0(1,:)>v_ub)/max(G0(1,G0(1,:)>v_ub))*max(v_ub(G0(1,:)>v_ub));G0(1,G0(1,:)<v_lb)=G0(1,G0(1,:)<v_lb)/min(G0(1,G0(1,:)<v_lb))*min(v_lb(G0(1,:)<v_lb));G01=G0;% -△xDx=Dx1;for j=1:dimtempX(j dim,:)=x_c;tempX(j dim,j)=x_c(1,j) Dx(j);if tempX(j dim,j)>ub(j)tempX(j dim,j)=ub(j);Dx(1,j)=tempX(j,j)-x_c(1,j);endif tempX(j dim,j)<lb(j)tempX(j dim,j)=lb(j);Dx(1,j)=tempX(j,j)-x_c(1,j);endtempY(j dim,:)=fobj(tempX(j,:),option,data);if tempY(j)*y_c<0g0(1,j)=(log(tempY(j))-log(y_c))./Dx(j); %EQ.2-18elsetemp=[tempY(j),y_c];temp=temp min(temp) eps;g0(1,j)=(log(temp(1))-log(temp(2)))./Dx(j);endg0(isnan(g0))=0;end G0=-g0(1,:)*norm(v_ub-v_lb)/2/norm(g0(1,:)); % part of Eq 2-18G0(1,G0(1,:)>v_ub)=G0(1,G0(1,:)>v_ub)/max(G0(1,G0(1,:)>v_ub))*max(v_ub(G0(1,:)>v_ub));G0(1,G0(1,:)<v_lb)=G0(1,G0(1,:)<v_lb)/min(G0(1,G0(1,:)<v_lb))*min(v_lb(G0(1,:)<v_lb));G02=G0;G0=G01+G02; % part of Eq 2-18G0(isnan(G0))=0;if sum(G0)==0N=numAgent-2*dim;Dm=mean(x-repmat(x_c,numAgent,1));Dm=norm(Dm); %EQ.2-22if Dm<norm(v_ub-v_lb)/20*iter/maxIterationDm=norm(v_ub-v_lb);endfor j=2*dim+(1:N)G0=randn(1,dim);tempX(j,:)=x(i,:)+5*rand*G0./norm(G0)*Dm; %EQ.2-21tempX(j,tempX(j,:)<lb)=lb(tempX(j,:)<lb);tempX(j,tempX(j,:)>ub)=ub(tempX(j,:)>ub);tempY(j,:)=fobj(tempX(j,:),option,data);endelseN=numAgent-2*dim;r1=exp(-10*(0:N-1)/(N-1));unitG=norm(Dx)/norm(G0); %EQ.2-19if unitG~=1r2=1:-(1-unitG)/(N-1):unitG;a=r1.*r2; %EQ.2-17elsea=r1;endfor j=2*dim+(1:N)tempX(j,:)=x_c+G0*a(j-2*dim); %EQ,2-20tempX(j,tempX(j,:)<lb)=lb(tempX(j,:)<lb);tempX(j,tempX(j,:)>ub)=ub(tempX(j,:)>ub);tempY(j,:)=fobj(tempX(j,:),option,data);endend[minY,no]=min(tempY);if minY<y_cy_c=tempY(no);x_c=tempX(no,:);endif rand>(no-dim*2)/(numAgent-dim*2)*(maxIteration-iter)/maxIterationgap=max(gapMin,gap-dec); %EQ.2-15endelseR1=rand(numAgent,dim);R2=rand(numAgent,dim);R3=rand(numAgent,dim);Rn=rand(numAgent,dim);indexR1=ceil(rand(numAgent,dim)*numAgent);indexR2=ceil(rand(numAgent,dim)*numAgent);std0=exp(-20*iter/maxIteration)*(v_ub-v_lb)/2;std1=std(x_m);% In order to use matrix operations, all individuals of the population are updated.% Although more individuals were updated, the running time of the algorithm dropped tremendously.% This is because MATLAB is extremely good at matrix operations.% If you want to rewrite this code in another language, we suggest you refer to AFO1.% AFO2 is optimized for MATLAB and may not be suitable for your language.for j=1:dimx_m1(:,j)=x_m(indexR1(:,j),j);x_m2(:,j)=x_m(indexR2(:,j),j);y_m1(:,j)=y_m(indexR1(:,j));y_m2(:,j)=y_m(indexR2(:,j));AI(:,j)=R1(:,j).*sign(y_m1(:,j)-y_m2(:,j)).*(x_m1(:,j)-x_m2(:,j));if std1(j)<=std0(j)position=find(AI(:,j)==0);AI(position,j)=Rn(position,j)*(v_ub(j)-v_lb(j))/2;position=find(AI(:,j)~=0);AI(position,j)=R2(:,j).*sign(y_m1(:,j)-y_m2(:,j)).*sign(x_m1(:,j)-x_m2(:,j))*(v_ub(j)-v_lb(j))/2;endendfor i=1:numAgentp =tanh(abs(y(i)-y_c)); %EQ.2-30if rand<p*(maxIteration-iter)/maxIteration% EQ 2-28At(i,:)=w2*At(i,:)+w4*R1(i,:).*(x_c-x(i,:))+w5*R2(i,:).*(x_m(i,:)-x(i,:));x(i,:)=x(i,:)+At(i,:); %EQ 2-29x(i,x(i,:)<lb)=lb(x(i,:)<lb);x(i,x(i,:)>ub)=ub(x(i,:)>ub);tempY(i,:)=y(i);y(i)=fobj(x(i,:),option,data);if tempY(i,:)<y(i)for j=1:dimr1=indexR1(i,j);r2=indexR2(i,j);v(i,j)=R3(i,j).*(x_m(r1,j)-x_m(r2,j))*-sign(y_m(r1)-y_m(r2));if std1(j)<=std0(j)if v(i,j)==0v(i,j)=randn*(v_ub(j)-v_lb(j))/2;elsev(i,j)=rand.*sign(x_m(r1,j)-x_m(r2,j))*-sign(y_m(r1)-y_m(r2))*(v_ub(j)-v_lb(j))/2;endendendendelsex(i,:)=x_c+AI(i,:);At(i,:)=AI(i,:);x(i,x(i,:)<lb)=lb(x(i,:)<lb);x(i,x(i,:)>ub)=ub(x(i,:)>ub);y(i)=fobj(x(i,:),option,data);endif y(i)<y_m(i)y_m(i)=y(i);x_m(i,:)=x(i,:);if y_m(i)<y_cy_c=y_m(i);x_c=x_m(i,:);At_c=At(i,:);endendendend% EQ.2-31if abs(y_c-recording.bestFit(iter))/abs(recording.bestFit(iter))<=pecount=count+1;elsecount=0;end%% 更新记录recording.bestFit(1+iter)=y_c;recording.meanFit(1+iter)=mean(y_m);% recording.std(1+iter)=mean(std(x_m));% recording.DC(1+iter)=norm(x_m-repmat(x_c,numAgent,1));% recording.x1(1+iter,:)=x(1,:);iter=iter+1;%%if count>Lfor i=1:numAgentx(i,:)=(ub-lb)*rand+lb;y(i)=fobj(x(i,:),option,data);if y(i)<y_m(i)y_m(i)=y(i);x_m(i,:)=x(i,:);if y_m(i)<y_cy_c=y_m(i);x_c=x_m(i,:);At_c=At(i,:);endendendcount=0;recording.bestFit(1+iter)=y_c;recording.meanFit(1+iter)=mean(y_m);iter=iter+1;endendbestY=y_c;bestX=x_c;end%%
3 仿真结果
4 参考文献
[1]李娜. 单亲遗传算法的冷链物流车辆路径问题(VRP)优化研究[D]. 燕山大学.
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部分理论引用网络文献，若有侵权联系博主删除。