matlab 算法程序.doc

一基于matlab Dijkstra算法的实现function l,z=Dijkstra(W)n = size (W,1);for i = 1 :n l(i)=W(1,i); z(i)=1;end i=1;while il(j)+W(j,i) l(i)=l(j)+W(j,i); z(i)=j; if jtffor i=1:Lfval_after,route_after=exchange(route,d);if fval_afterrandroute=route_after;fval=fval_after;else route=route;fval=fval;endendt=alpha*t;endtoc%-function fval=value(route,d)%used for reckoning the goal value of the selected traveling routen=length(d);fval=0;for i=1:n-1fval=fval+d(route(i),route(i+1);end%fval=fval+d(route(n),route(1);% if%is omited,it computes a circle,else%a chain-function fval_after,route_after=exchange(route,d)%changing traveling route by inversing the sequence between two selected 2 locations n=length(d);location1=ceil(n*rand);location2=ceil(n*rand);%the location of two exchanged numberloc1=min(location1,location2);loc2=max(location1,location2);middle_route=fliplr(route(loc1:loc2);%the part route which has been exchangedroute_after=route(1:loc1-1) middle_route route(loc2+1:n);%the after traveling routefval_after=value(route_after,d);%- 三/基于matlab TSP问题蚁群算法的实现load bayg29.txt;%29个城市的距离矩阵Dis = bayg29;CITY= length(bayg29);%CITY = 29;STEP = CITY; %时刻标记ECHO = 1000;ANT =5;T0 = 29*1601.-1;Q = 1; %fix 1 in ACSQ0 = 0.9; %Select DivideAlpha = 0.1; %Global UpdateP = 0.1; %Local UpdateA = 1; %fix 1B = 2; %Nij power% 各条路径的初始权值 for i=1:CITY; for j=1:i-1; T(i,j)=T0; TN(i,j)=(T(i,j).A)*(1/Dis(i,j).B); end; for j=i:CITY; T(i,j)=0; TN(i,j)=0; end; end; save(T.mat,T );% fid = fopen(T.txt,w);% fid2 = fopen(TN.txt,w);% fwrite(fid,T,integer*4);% fwrite(fid2,TN,integer*4);% fclose(fid);% fclose(fid2);for Echo=1:ECHO;% 设置起点 t = 1; %step1 for i =1:ANT; Mileage(i)=0; City(t,i) = ceil(CITY*rand(1);%1-29随机值 for j =2:STEP; % j此处表位置 %City(j,i)=mod(City(t,i)+j,CITY)+1; %wrong if City(t,i)+j-1 CITY % j此处表增量 City(j,i)= City(t,i)+j-1-CITY; else City(j,i)= City(t,i)+j-1; end; end; end; %pause; for t=2:STEP-1;%最后一步不需select过程 %选出下一城市 City(citypos,i) for i =1:ANT; base = City(t-1,i); q = 0; while q =0 q = rand(1); end; if q Q0 % AC select %Prob SUM(i)=0; for j=t:STEP %回路 SUM(i)=SUM(i)+TN(base,City(j,i)+TN(City(j,i),base); end; for j=1:STEP; if jt Prob(i,City(j,i)=0; else Prob(i,City(j,i)=(TN(base,City(j,i)+TN(City(j,i),base)/SUM(i); end; end; %round_select run =0; mile = 0; while run =0 run = rand(1); end; for j=t:STEP; mile=mile + Prob(i,City(j,i); if run (TN(base,City(citypos,i)+TN(City(citypos,i),base) citypos = j; end; end; end;%end of select %citypos is selected temp = City(t,i); City(t,i)=City(citypos,i); City(citypos,i)=temp; Mileage(i)=Mileage(i)+Dis(City(t-1,i),City(t,i)+Dis(City(t,i),City(t-1,i); %Local Update if City(t-1,i)City(t,i) T(City(t-1,i),City(t,i)=T(City(t-1,i),City(t,i)*(1-P)+T0*P; else T(City(t,i),City(t-1,i)=T(City(t,i),City(t-1,i)*(1-P)+T0*P; end; end; %for i =1:ANT; if Echo=1 save(T.mat,T,-APPEND); end; end;%for t=2:STEP-1; %last step. no need to select. for i =1:ANT; % sum the eage (last,first) Mileage(i)=Mileage(i)+Dis(City(STEP,i),City(1,i)+Dis(City(1,i),City(STEP,i); %Local Update if City(1,i)City(STEP,i) T(City(1,i),City(STEP,i)=T(City(1,i),City(STEP,i)*(1-P)+T0*P; else T(City(STEP,i),City(1,i)=T(City(STEP,i),City(1,i)*(1-P)+T0*P; end; end; if Echo=1 end; %Global Update. T,TN minant =1; for i=1:ANT; if Mileage(i)City(j+1,minant) T(City(j,minant),City(j+1,minant)=T(City(j,minant),City(j+1,minant)+(Q/Mileage(minant)*Alpha/(1-Alpha); else T(City(j+1,minant),City(j,minant)=T(City(j+1,minant),City(j,minant)+(Q/Mileage(minant)*Alpha/(1-Alpha); end; end; if City(1,minant)City(STEP,minant) T(City(1,minant),City(STEP,minant)=T(City(1,minant),City(STEP,minant)+(Q/Mileage(minant)*Alpha/(1-Alpha); else T(City(STEP,minant),City(1,minant)=T(City(STEP,minant),City(1,minant)+(Q/Mileage(minant)*Alpha/(1-Alpha); end; for i=1:CITY; for j=1:i-1; T(i,j)=T(i,j)*(1-Alpha); TN(i,j)=(T(i,j).A) *(1.0/Dis(i,j).B); end; end; if Echo=1 end;end %for Echo=1:ECHO; c,i=min(Mileage); save ACS.mat;四/基于matlab TSP问题遗传算法的实现%TSP问题（又名：旅行商问题，货郎担问题）遗传算法通用matlab程序%D是距离矩阵，n为种群个数,建议取为城市个数的12倍，%C为停止代数，遗传到第 C代时程序停止,C的具体取值视问题的规模和耗费的时间而定%m为适应值归一化淘汰加速指数 ,最好取为1,2,3,4 ,不宜太大%alpha为淘汰保护指数,可取为01之间任意小数,取1时关闭保护功能,最好取为0.81.0%R为最短路径,Rlength为路径长度function R,Rlength=geneticTSP(D,n,C,m,alpha)N,NN=size(D);farm=zeros(n,N);%用于存储种群for i=1:nfarm(i,:)=randperm(N);%随机生成初始种群endR=farm(1,:);%存储最优种群len=zeros(n,1);%存储路径长度fitness=zeros(n,1);%存储归一化适应值counter=0;while counter=alpha*randnn=nn+1;FARM(nn,:)=farm(i,:);endendFARM=FARM(1:nn,:);aa,bb=size(FARM);%交叉和变异while aanif nnnFARM=FARM(1:n,:);%保持种群规模为nendfarm=FARM;clear FARMcounter=counter+1endRlength=myLength(D,R);function a,b=intercross(a,b)L=length(a);if L=rand&L10W=ceil(L/10);else W=floor(L/10);endp=unidrnd(L-W+1);%随机选择交叉范围，从p到p+Wfor i=1:W%交叉x=find(a=b(1,p+i-1);y=find(b=a(1,p+i-1);a(1,p+i-1),b(1,p+i-1)=exchange(a(1,p+i-1),b(1,p+i-1);a(1,x),b(1,y)=exchange(a(1,x),b(1,y); endfunction x,y=exchange(x,y)temp=x;x=y;y=temp;% 计算路径的子程序function len=myLength(D,p)N,NN=size(D);len=D(p(1,N),p(1,1);for i=1:(N-1)len=len+D(p(1,i),p(1,i+1);end%计算归一化适应值子程序function fitness=fit(len,m,maxlen,minlen)fitness=len;for i=1:length(len)fitness(i,1)=(1-(len(i,1)-minlen)/(maxlen-minlen+0.000001).m;end 五/基于matlab的floyd算法function d,path=floyd(a,sp,ep)% floyd - 最短路问题 % % Syntax: d,path=floyd(a,sp,ep) % % Inputs: % a - 距离矩阵是指i到j之间的距离，可以是有向的 % sp - 起点的标号 % ep - 终点的标号 % % Outputs: % d - 最短路的距离 % path - 最短路的路径 % % Example: % a =% 0 50 Inf 40 25 10 ;% 50 0 15 20 Inf 25 ;% Inf 15 0 10 20 Inf ;% 40 20 10 0 10 25 ;% 25 Inf 20 10 0 55 ;% 10 25 Inf 25 55 0 ;% d,path=floyd(a,2,5)% % % Other m-files required: none % Subfunctions: none % MAT-files required: none % % See also: OTHER_FUNCTION_NAME1, OTHER_FUNCTION_NAME2 % Author: Xiaoyong Ren%Packaging Engineering, Xian University of Technology . % email : % QQ: 170071606 % Website: % December 2004; Last revision: 24-May-2005 %- BEGIN CODE - n=size(a,1);D=a;path=zeros(n,n);for i=1:n for j=1:n if D(i,j)=inf path(i,j)=j; %j是i的后续点 end endendfor k=1:n for i=1:n for j=1:n if D(i,j)D(i,k)+D(k,j) D(i,j)=D(i,k)+D(k,j); path(i,j)=path(i,k); end end endendp=sp;mp=sp;for k=1:n if mp=ep d=path(mp,ep); p=p,d; mp=d; endendd=D(sp,ep);path=p;%- END OF CODE -六/基于matlab灰色预测GM(1,1)实现function y,p,e=gm_1_1(X,k) %Build the calculating dieplate for the typical gray model. %Example y,p=gm_1_1(200 250 300 350,2) %Designed by NIXIUHUI,Dalian Fisher University. %20 April,2004. Last modified by NXH at 25 September,2004 if nargout3,error(Too many output argument.);end if nargin=1,k=1;x_orig=X; elseif nargin=0|nargin2 error(Wrong number of input arguments.); end x_orig=X; predict=k; %AGO process x=cumsum(x_orig); %compute the coefficient(a and u)- n=length(x_orig); %first generate the matrix B for i=1:(n-1); B(i)=-(x(i)+x(i+1)/2; end B=B ones(n-1,1); %then generate the matrix Y for i=1:(n-1); y(i)=x_orig(i+1); end Y=y; %get the coefficient. a=au(1) u=au(2) au=(inv(B*B)*(B*Y); %- %change the grey model to symbolic expression coef1=au(2)/au(1); coef2=x_orig(1)-coef1; coef3=0-au(1); costr1=num2str(coef1); costr2=num2str(abs(coef2); costr3=num2str(coef3); eq=strcat(costr1,+,costr2,e,costr3,*(t-1); %comparison of calculated and observed value for t=1:n+predict mcv(t)=coef1+coef2*exp(coef3*(t-1); end x_mcv0=diff(mcv); x_mcve=x_orig(1) x_mcv0; x_mcv=diff(mcv(1:end-predict); x_orig_n=x_orig(2:end); x_c_error=x_orig_n-x_mcv; x_error=mean(abs(x_c_error./x_orig_n); if x_error0.2 disp(model disqualification!); elseif x_error0.1 disp(model check out); else disp(model is perfect!); end %predicting model and plot gragh plot(1:n,x_orig,diamond,1:n+predict,x_mcve); p=x_mcve(end-predict+1:end); xlabel(CURVE OF GREY MODEL ANALYSIS); title(GM(1,1); grid on y=eq; e=x_error; p=x_mcve(end-predict+1:end);七/基于matlab灰色关联度计算的实现function r=incident_degree(x0,x1) %compute the incident degree for grey model. %Designed by NIXIUHUI,Dalian Fisher University. %17 August,2004,Last modified by NXH at 21 August,2004 %数据初值化处理 x0_initial=x0./x0(1); temp=size(x1); b=repmat(x1(:,1),1 temp(2); x1_initial=x1./b; %分辨系数选择 K=0.1; disp(The grey interconnect degree is: ); x0_ext=repmat(x0_initial,temp(1) 1); contrast_mat=abs(x0_ext-x1_initial); delta_min=min(min(contrast_mat);%delta_min在数据初值化后实际为零 delta_max=max(max(contrast_mat); a=delta_min+K*delta_max; incidence_coefficient=a./(contrast_mat+K*delta_max);%得到关联系数 r=(sum(incidence_coefficient)/temp(2); %得到邓氏面积关联度八/基于matlab层次分析法的实现disp(请输入判断矩阵A(n阶);A=input(A=);n,n=size(A);x=ones(n,100);y=ones(n,100);m=zeros(1,100);m(1)=max(x(:,1);y(:,1)=x(:,1);x(:,2)=A*y(:,1);m(2)=max(x(:,2);y(:,2)=x(:,2)/m(2);p=0.0001;i=2;k=abs(m(2)-m(1);while kp i=i+1; x(:,i)=A*y(:,i-1); m(i)=max(x(:,i); y(:,i)=x(:,i)/m(i); k=abs(m(i)-m(i-1);enda=sum(y(:,i);w=y(:,i)/a;t=m(i);disp(w);disp(t); %以下是一致性检验CI=(t-n)/(n-1);RI=0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59;CR=CI/RI(n);if CRx(j) temp=x(i); x(i)=x(j); x(j)=temp; end end end y=x;十/matlab遗传算法实例讲解（转引）【问题】求f(x)=x+10*sin(5x)+7*cos(4x)的最大值，其中0=x=9 【分析】选择二进制编码，种群中的个体数目为10，二进制编码长度为20，交叉概率为0.95,变异概率为0.08 【程序清单】 %编写目标函数 functionsol,eval=fitness(sol,options) x=sol(1); eval=x+10*sin(5*x)+7*cos(4*x); %把上述函数存储为fitness.m文件并放在工作目录下initPop=initializega(