智能优化算法程序代码集锦

1、人工蚂蚁算法%function x,y, minvalue = AA(func) % Example x, y,minvalue = AA(Foxhole) clc; tic; subplot(2,2,1); % plot 1draw(func);title(func, Function);%初始化各参数Ant=100;%蚂蚁规模ECHO=200;%迭代次数 step=0.01*rand(1);%局部搜索时的步长temp=0,0;%各子区间长度start1=-100;end1=100;start2=-100;end2=100;Len1=(end1-start1)/Ant;Len2=(end2-

2、start2)/Ant;%P = 0.2;%初始化蚂蚁位置 for i=1:AntX(i,1)=(start1+(end1-start1)*rand(1);X(i,2)=(start2+(end2-start2)*rand(1);%func=AA_Foxhole_Func(X(i,1),X(i,2);val=feval(func,X(i,1),X(i,2); T0(i)=exp(-val);%初始信息素,随函数值大,信息素浓度小,反之亦然 %* end; %至此初始化完成for Echo=1:ECHO %开始寻优 %P0函数定义,P0为全局转移选择因子a1=0.9;b1=(1/ECHO)*2*

3、log(1/2);f1=a1*exp(b1*Echo);a2=0.225;b2=(1/ECHO)*2*log(2);f2=a2*exp(b2*Echo);if Echo=T_Best T_Best=T0(j); BestIndex=j; end; end; W=Wmax-(Wmax-Wmin)*(Echo/ECHO); %局部搜索步长更新参数 for j_g=1:Ant %全局转移概率求取,当该蚂蚁随在位置不是bestindex时 if j_g=BestIndex r=T0(BestIndex)-T0(j_g); Prob(j_g)=exp(r)/exp(T0(BestIndex); else

4、 %当j_g=BestIndex的时候进行局部搜索 if rand(1)exp(-F1_B) X(BestIndex,1)=temp(1,1); X(BestIndex,2)=temp(1,2); end; end; for j_g_tr=1:Ant if Prob(j_g_tr)= 2 if min_local(Echo)min_global(Echo-1) min_global(Echo)=min_local(Echo); else min_global(Echo)=min_global(Echo-1); end; else min_global(Echo)=minvalue_iter;

5、end; subplot(2,2,4);% Plot 3 min_global=min_global; index(:,1)=1:ECHO; plot(Echo, min_global(Echo),y*) %axis(0 ECHO 0 10); hold on; title (func, (X) = , num2str(minvalue_iter),Color,r); xlabel(iteration); ylabel(f(x); grid on; end;%ECHO循环结束 c_max,i_max=max(T0); minpoint=X(i_max,1),X(i_max,2); %func3

6、 = AA_Foxhole_Func(X(i_max,1),X(i_max,2); %* %minvalue = func3; minvalue=feval(func,X(i_max,1),X(i_max,2); x=X(BestIndex,1); y=X(BestIndex,2); runtime=toc人工免疫算法function x,y,fx,vfx,vmfit,P,vpm = AI(func,gen,n,pm,per); % Example x,y,fx = AI(Foxhole)subplot(2,2,1);draw(func);title( func, Function);if n

7、argin = 1, % gen = 200; n = round(size(P,1)/2); pm = 0.0005; per = 0.0; fat = 10; %gen = 250; n = size(P,1); pm = 0.01; per = 0.0; fat = .1; P = cadeia(200,44,0,0,0); gen = 40; n = size(P,1); pm = 0.2; per = 0.0; fat = 0.1;end; while n = 0, n = input(n has to be at least one. Type a new value for n:

8、 );end; xmin=-100;xmax=100;ymin=-100;ymax=100; x = decode(P(:,1:22),xmin,xmax); y = decode(P(:,23:end),ymin,ymax); %fit = eval(f);%fit=AI_Foxhole_Func(x,y);%fit=feval(func,x y);%imprime(1,vxp,vyp,vzp,x,y,fit,1,1); % Hypermutation controlling parameterspma = pm; itpm = gen; pmr = 0.8; % General defin

9、tionsvpm = ; vfx = ; vmfit = ; valfx = 1; N,L = size(P); it = 0; PRINT = 1; % Generationswhile it = gen & valfx = 100, x = decode(P(:,1:22),xmin,xmax); y = decode(P(:,23:end),ymin,ymax); T = ; cs = ; %fit = eval(f); %fit=AI_Foxhole_Func(x,y);% fit=feval(func,x y); a,ind = sort(fit); valx = x(ind(end

10、-n+1:end); valy = y(ind(end-n+1:end); fx = a(end-n+1:end); % n best individuals (maximization) % Reproduction T,pcs = reprod(n,fat,N,ind,P,T); % Hypermutation M = rand(size(T,1),L) actual values% vxplot, vplot - original (base) functionif PRINT = 1, if rem(it,mit) = 0, mesh(vx,vy,vz); hold on; axis(

11、-100 100 -100 100 0 500); xlabel(x); ylabel(y); zlabel(f(x,y); plot3(x,y,fx,k*); drawnow; hold off; end;end; % Reproductionfunction T,pcs = reprod(n,fat,N,ind,P,T);% n - number of clones% fat - multiplying factor% ind - best individuals% T - temporary population% pcs - final position of each cloneif

12、 n = 1, cs = N; T = ones(N,1) * P(ind(1),:);else, for i=1:n, % cs(i) = round(fat*N/i); cs(i) = round(fat*N); pcs(i) = sum(cs); T = T; ones(cs(i),1) * P(ind(end-i+1),:); end;end; % Control of pmfunction pm = pmcont(pm,pma,pmr,it,itpm);% pma - initial value% pmr - control rate% itpm - iterations for r

13、estoringif rem(it,itpm) = 0, pm = pm * pmr; if rem(it,10*itpm) = 0, pm = pma; end;end; % Decodify bitstringsfunction x = decode(v,min,max);% x - real value (precision: 6)% v - binary string (length: 22)v = fliplr(v); s = size(v);aux = 0:1:21; aux = ones(s(1),1)*aux;x1 = sum(v.*2.aux);x = min + (max-

14、min)*x1 ./ ;function ab,ag = cadeia(n1,s1,n2,s2,bip)%default parameter value seetingif nargin = 2, n2 = n1; s2 = s1; bip = 1;elseif nargin = 4, bip = 1;end;% Antibody (Ab) chainsab = 2 .* rand(n1,s1) - 1;%create n1 row s1 column array, its value range is between -1 or 1if bip = 1, ab = hardlims(ab);

15、else, ab = hardlim(ab);end;% Antigen (Ag) chainsag = 2 .* rand(n2,s2) - 1;if bip = 1, ag = hardlims(ag);else, ag = hardlim(ag);end;% End Function CADEIA%-免疫粒子群优化算法（Artificial Immune - Particle Swarm Optimization） function x,y,Result=PSO_AI(func)% Example x, y,minvalue = PSO_AI(Foxhole)clc; subplot(2

16、,2,1); % plot 1draw(func);title(func, Function);ticformat long;%-给定初始化条件-c1=1.4962; %学习因子1c2=1.4962; %学习因子2w=0.7298; %惯性权重MaxDT=200; %最大迭代次数D=2; %搜索空间维数（未知数个数）N=100; %初始化群体个体数目eps=10(-20); %设置精度(在已知最小值时候用)DS=10; %每隔DS次循环就检查最优个体是否变优replaceP=0.6; %粒子的概率大于replaceP将被免疫替换minD=1e-015; %粒子间的最小距离Psum=0; %个体

17、最佳的和range=100;count = 0;%-初始化种群的个体- for i=1:N for j=1:D x(i,j)=-range+2*range*rand; %随机初始化位置 v(i,j)=randn; %随机初始化速度 endend %-先计算各个粒子的适应度，并初始化Pi和Pg- for i=1:N %p(i)=Foxhole(x(i,:),D); %fitness是计算各个粒子适应度的函数，见文件fitness.m %* p(i)=feval(func,x(i,:); y(i,:)=x(i,:);endpg=x(1,:); %Pg为全局最优for i=2:N if feval(

18、func,x(i,:)feval(func,pg) %* pg=x(i,:); endend %-进入主要循环，按照公式依次迭代，直到满足精度要求- for t=1:MaxDT for i=1:N v(i,:)=w*v(i,:)+c1*rand*(y(i,:)-x(i,:)+c2*rand*(pg-x(i,:); x(i,:)=x(i,:)+v(i,:); if feval(func,x(i,:)p(i) %* p(i)=feval(func,x(i,:); %* y(i,:)=x(i,:); end if p(i)feval(func,pg) %* pg=y(i,:); subplot(2,

19、2,2); % Plot 1 bar(pg,0.25); axis(0 3 -40 40 ) ; title (Iteration , num2str(t); pause (0.1); subplot(2,2,3); % Plot 2 plot(pg(1,1),pg(1,2),rs,MarkerFaceColor,r, MarkerSize,8) hold on; plot(x(:,1),x(:,2),k.); set(gca,Color,g) hold off; grid on; axis(-100 100 -100 100 ) ; title(Global Min = ,num2str(p

20、(i); xlabel(Min_x= ,num2str(pg(1,1), Min_y= ,num2str(pg(1,2); end end Pbest(t)=feval(func,pg) ; %* % if Foxhole(pg,D)DS if mod(t,DS)=0 & (Pbest(t-DS+1)-Pbest(t)1e-020 %如果连续DS代数，群体中的最优没有明显变优，则进行免疫. %在函数测试的过程中发现，经过一定代数的更新，个体最优不完全相等，但变化非常非常小， %我认为这个时候也应用免疫了，所以我没有用“Pbest(t-DS+1)Pbest(t)”作为判断条件， %不过“(Pbe

21、st(t-DS+1)-Pbest(t)1e-020”是否合理也值得探讨。 for i=1:N %先计算出个体最优的和 Psum=Psum+p(i); end for i=1:N %免疫程序 for j=1:N %计算每个个体与个体i的距离 distance(j)=abs(p(j)-p(i); end num=0; for j=1:N %计算与第i个个体距离小于minD的个数 if distance(j)replaceP x(i,:)=-range+2*range*rand(1,D); subplot(2,2,4); axi; % Plot 4 plot(x(i,1),x(i,2),k*); g

22、rid on; axis(-100 100 -100 100 ) ; title(New Min = ,num2str( feval(func,x(i,:); %* xlabel(Immune ,num2str(count); pause (0.2); count=count+1; end end end end end %-最后给出计算结果 x=pg(1,1);y=pg(1,2); Result=feval(func,pg); %* toc%-算法结束-function probabolity(N,i)PF=p(N-i)/Psum;%适应度概率disp(PF);for jj=1:N dist

23、ance(jj)=abs(P(jj)-P(i);endnum=0;for ii=1:N if distance(ii)minD num=num+1; endendPD=num/N; %个体浓度PR=a*PF+(1-a)*PD; %替换概率% result=PR;差分进化算法function sol= DE(func) % Example sol= DE(Foxhole)ticpopsize= 100;lb=-100 -100;ub=100 100; sol = diffevolve(func, popsize, lb, ub);tocend function sol, fval, evals

24、= diffevolve(varargin)%DIFFEVOLVE Differential Evolution Optimization% Usage:% sol = DIFFEVOLVE(PROBLEM)% sol = DIFFEVOLVE(func, popsize, lb, ub) % sol = DIFFEVOLVE(func, popsize, lb, ub, option1, value1, .) % sol, fval = DIFFEVOLVE(.)% sol, fval, evals = DIFFEVOLVE(.)% % DIFFEVOLVE(func, popsize, l

25、b, ub) tries to find the global optimum of % the fitness-function func, using a transversal differential evolution % strategy. The population size set by popsize, and the boundaries for% each dimension are set by the vectors lb and ub, respectively. % sol, fval, evals = DIFFEVOLVE(.) returns the trial vector found to % yield the global minimum in sol, and the corresponding function value % by fval. The total amount of function evaluations that the algorithm% performed is returned in evals.% The function func must accept a vector argument of length N, equal to % the number of