使用AFO算法以及GA和PSO算法求解不确定多式联运路径优化问题（Matlab）

function [bestY,bestX,recording]=AFO2(x,y,option,data) % Update log. % 1. 2021.1.1 % Version 1.0 % All experiments of the paper are run based on this version, except for the experiments of running time. % 2. 2021.1.7 % Version 1.1 % Disadvantages of version 1.0 % (1) Too slow % (2) The total number of evaluations is T*(N+m) after the catastrophe strategy is triggered, and m is the number of times the catastrophe strategy is triggered. % However, this problem will not affect the results of this experiment because the maximum number of iterations of the experiment is 50, and the catastrophe strategy will basically not be triggered. % The runtime experiments of the paper are based on this version % Updated content. % (1) Optimization based on the advantages of MATLAB. The problem of too slow speed is solved. % The core reason for the excessive slowness was that strategy 2 did not use matrix operations in version 1.0. % Note: In order to use matrix operations, this version updates all individuals of the population when using the third strategy, but calculates the fitness value only for those individuals that are eligible. The total number of evaluations is still T*N. % (2) After using the catastrophe strategy, the current iteration number +1,the total evaluation number reverts to T*N %% Authority % Author: Zhe Yang % E-mail: 454170989@qq.com % School: University of Manchester %% 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 %% initialization pe=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 III w4=option.w4;%weight of Moving strategy III w5=option.w5;%weight of Moving strategy III pe=option.pe; % rate to judge Premature convergence gapMin=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 population y_m=y; x_m=x; %% update recording recording.bestFit=y_c; recording.meanFit=mean(y_m); %% main loop iter=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 meanD=mean(abs(repmat(x_c,numAgent,1)-x)); for i=1:numAgent tempX(i,:)=x_c+randn(1,dim).*meanD; tempY(i,:)=fobj(tempX(i,:)); end [minY,no]=min(tempY); if minY<y_c y_c=tempY(no); x_c=tempX(no,:); end if rand>(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 rand<p*(maxIteration-iter)/maxIteration % EQ 2-28 At(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 2-29 x(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,:)); if tempY(i,:)<y(i) for j=1:dim r1=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)==0 v(i,j)=randn*(v_ub(j)-v_lb(j))/2; else v(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; end end end end else x(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,:)); end if y(i)<y_m(i) y_m(i)=y(i); x_m(i,:)=x(i,:); if y_m(i)<y_c y_c=y_m(i); x_c=x_m(i,:); At_c=At(i,:); end end end end % EQ.2-31 if abs(y_c-recording.bestFit(iter))/abs(recording.bestFit(iter))<=pe count=count+1; else count=0; end %% ¸üмǼ 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>L for i=1:numAgent x(i,:)=(ub-lb)*rand+lb; y(i)=fobj(x(i,:)); if y(i)<y_m(i) y_m(i)=y(i); x_m(i,:)=x(i,:); if y_m(i)<y_c y_c=y_m(i); x_c=x_m(i,:); At_c=At(i,:); end end end count=0; recording.bestFit(1+iter)=y_c; recording.meanFit(1+iter)=mean(y_m); iter=iter+1; end end bestY=y_c; bestX=x_c; end %%