基于遺傳算法的車間布局優(yōu)化MATLAB源碼

基于遺傳算法的車間布局優(yōu)化MATLAB源碼車間布局優(yōu)化是將加工設(shè)備、物料輸送設(shè)備、工作單元和通道走廊等布置物體合理地放置在一個(gè)有限的生產(chǎn)車間內(nèi)的過程。車間布局優(yōu)化模型是一個(gè)含有較為復(fù)雜約束的非線性連續(xù)優(yōu)化模型，可以使用遺傳算法進(jìn)行優(yōu)化。%%車間布局遺傳禁忌搜索算法仿真主界面%%第一步：設(shè)置問題實(shí)例Li=[38;16;30;40;48;32;46];%Wi=[28;36;16;18;24;28;16];%車間長度車間寬度%每單位距離每單位物流量的物料搬運(yùn)費(fèi)用P=[0,2,3,2,5,4,4;0,0,5,2,2,3,4;0,0,0,1,5,4,3;0,0,0,0,1,5,1;0,0,0,0,0,4,5;0,0,0,0,0,0,1;0,0,0,0,0,0,0];%物料搬運(yùn)的頻率F=[0,2,2,1,0,2,1;0,0,2,1,1,2,2;0,0,0,2,1,2,1;0,0,0,0,2,1,2;0,0,0,0,0,2,1;0,0,0,0,0,0,1;0,0,0,0,0,0,0];%物流量Q=[0,10,6,8,4,6,1;0,0,3,2,5,4,4;0,0,0,6,8,6,5;0,0,0,0,5,8,1;0,0,0,0,0,8,1;0,0,0,0,0,0,5;0,0,0,0,0,0,0];%物料搬運(yùn)速率V=[0,4,4,4,4,4,4;0,0,2,2,2,2,2;0,0,0,2,2,2,2;0,0,0,0,3,3,3;0,0,0,0,0,3,3;0,0,0,0,0,0,2;0,0,0,0,0,0,0];L=200;%矩形區(qū)域的長度，x軸W=120;%矩形區(qū)域的寬度，y軸minDX=10;%各車間的最小水平間距minDY=10;%各車間的最小垂直間距minDS=10;%各車間到區(qū)域邊界的最小距離%%pop_size=400;max_gen=500;Pm=0.3;kc=0.5;kt=0.5;PLambda=1000;PK=1000;n=size(P,1);LB=zeros(2*n,1);UB=zeros(2*n,1);fori=1:nLB(2*i-1)=0.5*Li(i)+minDS;LB(2*i)=0.5*Wi(i)+minDS;UB(2*i-1)=L-0.5*Li(i)-minDS;UB(2*i)=W-0.5*Wi(i)-minDS;end%%調(diào)用遺傳算法figure(3)[BESTX,BESTY,ALLX,ALLY]=GAUCP2(max_gen,pop_size,Pm,LB,UB,L,W,Li,Wi,P,F,Q,V,minDX,minDY,kc,kt,PLambda,PK);X=BESTX{max_gen};disp('遺傳算法輸出的最優(yōu)結(jié)果為');disp(X);figure(4)PlotFigure(X,Li,Wi,L,W);function[BESTX,BESTY,ALLX,ALLY]=GAUCP2(K,N,Pm,LB,UB,PL,PW,PLi,PWi,PP,PF,PQ,PV,PminDX,PminDY,Pkc,Pkt,PLambda,PK)%%此函數(shù)實(shí)現(xiàn)遺傳算法，用于車間布局優(yōu)化%%輸入?yún)?shù)列表%K 迭代次數(shù)%N 種群規(guī)模，要求是偶數(shù)%Pm變異概率

%LB決策變量的下界，M×1的向量%UB決策變量的上界，M×1的向量%%輸出參數(shù)列表%BESTX K×1細(xì)胞結(jié)構(gòu)，每一個(gè)元素是M×1向量，記錄每一代的最優(yōu)個(gè)體%BESTY K×1矩陣，記錄每一代的最優(yōu)個(gè)體的評(píng)價(jià)函數(shù)值%ALLX K×1細(xì)胞結(jié)構(gòu)，每一個(gè)元素是M×N矩陣，記錄全部個(gè)體%ALLY K×N矩陣，記錄全部個(gè)體的評(píng)價(jià)函數(shù)值%%第一步：M=length(LB);%決策變量的個(gè)數(shù)%種群初始化，每一列是一個(gè)樣本farm=zeros(M,N);fori=1:Mx=unifrnd(LB(i),UB(i),1,N);farm(i,:)=x;end%輸出變量初始化ALLX=cell(K,1);%ALLY=zeros(K,N);%KBESTX=cell(K,1);%BESTY=zeros(K,1);%K細(xì)胞結(jié)構(gòu)，每一個(gè)元素是MALLX=cell(K,1);%ALLY=zeros(K,N);%KBESTX=cell(K,1);%BESTY=zeros(K,1);%K×N矩陣，記錄每一代評(píng)價(jià)函數(shù)值細(xì)胞結(jié)構(gòu)，每一個(gè)元素是M×1向量，記錄每一代的最優(yōu)個(gè)體×1矩陣，記錄每一代的最優(yōu)個(gè)體的評(píng)價(jià)函數(shù)值k=1;%迭代計(jì)數(shù)器初始化%%第二步：迭代過程whilek<=K%%以下是交叉過程newfarm=zeros(M,2*N);Ser=randperm(N);%兩兩隨機(jī)配對(duì)的配對(duì)表A=farm(:,Ser(1));B=farm(:,Ser(2));P0=unidrnd(M-1);a=[A(1:P0,:);B((P0+1):end,:)];% 產(chǎn)生子代ab=[B(1:P0,:);A((P0+1):end,:)];% 產(chǎn)生子代bnewfarm(:,2*N-1)=a;%加入子代種群newfarm(:,2*N)=b;fori=1:(N-1)A=farm(:,Ser(i));B=farm(:,Ser(i+1));P0=unidrnd(M-1);a=[A(1:P0,:);B((P0+1):end,:)];b=[B(1:P0,:);A((P0+1):end,:)];newfarm(:,2*i-1)=a;newfarm(:,2*i)=b;endFARM=[farm,newfarm];%%選擇復(fù)制SER=randperm(3*N);FITNESS=zeros(1,3*N);fitness=zeros(1,N);fori=1:(3*N)Beta=FARM(:,i);SE=FIT(Beta,PL,PW,PLi,PWi,PP,PF,PQ,PV,PminDX,PminDY,Pkc,Pkt,PLambda,PK);FITNESS(i)=SE;endfori=1:Nf1=FITNESS(SER(3*i-2));f2=FITNESS(SER(3*i-1));f3=FITNESS(SER(3*i));iff1<=f2&&f1<=f3farm(:,i)=FARM(:,SER(3*i-2));fitness(:,i)=FITNESS(:,SER(3*i-2));elseiff2<=f1&&f2<=f3farm(:,i)=FARM(:,SER(3*i-1));fitness(:,i)=FITNESS(:,SER(3*i-1));elsefarm(:,i)=FARM(:,SER(3*i));fitness(:,i)=FITNESS(:,SER(3*i));endend%%記錄最佳個(gè)體和收斂曲線X=farm;Y=fitness;ALLX{k}=X;ALLY(k,:)=Y;minY=min(Y);pos=find(Y==minY);BESTX{k}=X(:,pos(1));BESTY(k)=minY;%%變異fori=1:NifPm>rand&&pos(1)~=iAA=farm(:,i);BB=GaussMutation(AA,LB,UB);forj=1:MBB(j,1)=unifrnd(LB(j),UB(j),1,1);endfarm(:,i)=BB;endenddisp(k);k=k+1;end%%繪圖BESTY2=BESTY;BESTX2=BESTX;fork=1:KTempY=BESTY(1:k);minTempY=min(TempY);posY=find(TempY==minTempY);BESTY2(k)=minTempY;BESTX2{k}=BESTX{posY(1)};endBESTY=BESTY2;BESTX=BESTX2;MeanBESTY=mean(ALLY');plot(-BESTY,'-ks','MarkerEdgeColor','k','MarkerFaceColor','k','MarkerSize',1)holdonplot(-MeanBESTY,'-ro','MarkerEd