【优化选址】基于帝国企鹅算法求解工厂-中心-需求点三级选址问题附matlab代码

【优化选址】基于帝国企鹅算法求解工厂-中心-需求点三级选址问题附matlab代码

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=y_c;recording.meanFit=mean(y_m);%% main loopiter=1;while iter<=maxIteration %Dmp(['AFO,iter:',num2str(iter),',minFit:',num2str(y_c)]) %% Moving Strategy I for center of population if rem(iter, gap)==0 c0=exp(-30*(iter-gap0)/maxIteration); % EQ.2-11 Dx=ones(1,dim); Dx=c0*Dx/norm(Dx)*norm(v_ub-v_lb)/2; %EQ.2-12 %+¡÷x Dx1=-Dx; %-¡÷x % +¡÷x for j=1:dim tempX(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); end if tempX(j,j)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,:)ub(j) tempX(j+dim,j)=ub(j); Dx(1,j)=tempX(j,j)-x_c(1,j); end if tempX(j+dim,j)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,:)ub)=ub(tempX(j,:)>ub); tempY(j,:)=fobj(tempX(j,:)); end else N=numAgent-2*dim; r1=exp(-10*(0:N-1)/(N-1)); unitG=norm(Dx)/norm(G0); %EQ.2-19 if unitG~=1 r2=1:-(1-unitG)/(N-1):unitG; a=r1.*r2; %EQ.2-17 else a=r1; end for j=2*dim+(1:N) tempX(j,:)=x_c+G0*a(j-2*dim); %EQ,2-20 tempX(j,tempX(j,:)ub)=ub(tempX(j,:)>ub); tempY(j,:)=fobj(tempX(j,:)); end end [minY,no]=min(tempY); if minY(no-dim*2)/(numAgent-dim*2)*(maxIteration-iter)/maxIteration gap=max(gapMin,gap-dec); %EQ.2-15 end else R1=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:dim x_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; end end for i=1:numAgent p =tanh(abs(y(i)-y_c)); %EQ.2-30 if randub)=ub(x(i,:)>ub); tempY(i,:)=y(i); y(i)=fobj(x(i,:)); if tempY(i,:)ub)=ub(x(i,:)>ub); y(i)=fobj(x(i,:)); end if y(i)L for i=1:numAgent x(i,:)=(ub-lb)*rand+lb; y(i)=fobj(x(i,:)); if y(i)

3 仿真结果

4 参考文献

[1]李卫江, 郭晓汾, 张毅,等. 基于Matlab优化算法的物流中心选址[J]. 长安大学学报（自然科学版）, 2006, 026(003):76-79.

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