基于MATLAB实现的用人工鱼群算法求解梯级水库优化调度，只需要修改相应的约束条件就可以进行优化计算了+使用说明文档.zip

%人工鱼群算法 梯级水库调度 tic clear all clc format long Visual=2.5; Step=0.3; N=100; %50条鱼 Try_number=100;%觅食时做50次搜索 d=[]; h=1e-1; Friend_number=50; a=[977,977,977,977,977,977,977,957,977,970,977,977]; %%[898,898,898,898,898,898,893,892,892,898,898,898]; b=[970,970,970,970,952,952,952,952,952,952,970,970]; %%[888,888,888,888,888,893,888,888,888,888,888,888]; k=0; %最外围变量，m为其上限，控制循环次数 m=50; X1=rand(N,1)*(a(1)-b(1))+b(1); X2=rand(N,1)*(a(2)-b(2))+b(2); X3=rand(N,1)*(a(3)-b(3))+b(3); X4=rand(N,1)*(a(4)-b(4))+b(4); X5=rand(N,1)*(a(5)-b(5))+b(5); X6=rand(N,1)*(a(6)-b(6))+b(6); X7=rand(N,1)*(a(7)-b(7))+b(7); X8=rand(N,1)*(a(8)-b(8))+b(8); X9=rand(N,1)*(a(9)-b(9))+b(9); X10=rand(N,1)*(a(10)-b(10))+b(10); X11=rand(N,1)*(a(11)-b(11))+b(11); X12=rand(N,1)*(a(12)-b(12))+b(12); X=[X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12];%人工鱼数量50，一行24个向量为一条鱼 for i=1:N wwww=[X(i,1),X(i,2),X(i,3),X(i,4),X(i,5),X(i,6),X(i,7),X(i,8),X(i,9),X(i,10),X(i,11),X(i,12)]; %www为所有鱼的全体，一行24个值，总共50行 d(i)=maxf(wwww); end [w,i]=max(d); maxX=[X(i,1),X(i,2),X(i,3),X(i,4),X(i,5),X(i,6),X(i,7),X(i,8),X(i,9),X(i,10),X(i,11),X(i,12)]; maxY=w; %初始公告板记录 w即为最大函数值 figurex1=[];figurex2=[];figurex3=[];figurex4=[];figurex5=[];figurex6=[];figurex7=[];figurex8=[];figurex9=[];figurex10=[];figurex11=[];figurex12=[]; figurez=[]; figurex1(numel(figurex1)+1)=maxX(1); %figurex1(numel(figurex1)+1)即为figurex1(1)，也即X(i,1)，同14行 figurex2(numel(figurex2)+1)=maxX(2); figurex3(numel(figurex3)+1)=maxX(3);figurex4(numel(figurex4)+1)=maxX(4);figurex5(numel(figurex5)+1)=maxX(5);figurex6(numel(figurex6)+1)=maxX(6); figurex7(numel(figurex7)+1)=maxX(7);figurex8(numel(figurex8)+1)=maxX(8);figurex9(numel(figurex9)+1)=maxX(9);figurex10(numel(figurex10)+1)=maxX(10); figurex11(numel(figurex11)+1)=maxX(11);figurex12(numel(figurex12)+1)=maxX(12); figurez(numel(figurez)+1)=maxY; %函数最大值 kkk=0; for p=1:3 while(k<m) for i=1:N %一次一条鱼进行 XX=[X(i,1),X(i,2),X(i,3),X(i,4),X(i,5),X(i,6),X(i,7),X(i,8),X(i,9),X(i,10),X(i,11),X(i,12)]; nf=0; Xc=0; for j=1:N %聚群行为开始 XXX=[X(j,1),X(j,2),X(j,3),X(j,4),X(j,5),X(j,6),X(j,7),X(j,8),X(j,9),X(j,10),X(j,11),X(j,12)]; if(norm(XXX-XX)<Visual) nf=nf+1; Xc=Xc+XXX; end end Xc=Xc/nf; %鱼群中心位置 if((maxf(Xc))>maxf(XX)) XXnext1=XX+rand*Step*(Xc-XX)/norm(Xc-XX); XXnext1=xianzhi(XXnext1); else XXnext1=gmjprey(XX,Try_number,Visual,Step); %觅食 XXnext1=xianzhi(XXnext1); end%聚群行为结束 %maxX=XX;%追尾行为开始，追尾初始位置仍为xx和聚群一样的，同第35行 %maxY=maxf(XX); for j=1:Friend_number XXX=[X(j,1),X(j,2),X(j,3),X(j,4),X(j,5),X(j,6),X(j,7),X(j,8),X(j,9),X(j,10),X(j,11),X(j,12)]; if(norm(XXX-XX)<Visual & maxf(XXX)>maxY) maxX=XXX; maxY=maxf(XXX); %此时maxf为Friend_number中的最大值 end end if((maxY)>maxf(XX)) XXnext2=XX+rand*Step*(maxX-XX)/norm(maxX-XX); XXnext2=xianzhi(XXnext2); else XXnext2 =gmjprey(XX,Try_number,Visual,Step);%觅食 XXnext2=xianzhi(XXnext2); end%追尾行为结束 if(maxf(XXnext1)>maxf(XXnext2)) %聚群与追尾进行对比 for j=1:12 X(i,j)=XXnext1(j); end else for j=1:12 X(i,j)=XXnext2(j); end end end %一次迭代结束 for i=1:N XXXX=[X(i,1),X(i,2),X(i,3),X(i,4),X(i,5),X(i,6),X(i,7),X(i,8),X(i,9),X(i,10),X(i,11),X(i,12)]; if maxf(XXXX)>maxY %和公告板进行对比，同25行，比较 maxY=maxf(XXXX); maxX=XXXX; figurex1(numel(figurex1)+1)=maxX(1);figurex2(numel(figurex2)+1)=maxX(2); figurex3(numel(figurex3)+1)=maxX(3);figurex4(numel(figurex4)+1)=maxX(4);figurex5(numel(figurex5)+1)=maxX(5);figurex6(numel(figurex6)+1)=maxX(6); figurex7(numel(figurex7)+1)=maxX(7);figurex8(numel(figurex8)+1)=maxX(8);figurex9(numel(figurex9)+1)=maxX(9);figurex10(numel(figurex10)+1)=maxX(10); figurex11(numel(figurex11)+1)=maxX(11);figurex12(numel(figurex12)+1)=maxX(12); figurez(numel(figurez)+1)=maxY; end end k=k+1; %进入下一次迭代 end a1(p)=maxY; end u=length(a1); for i=1:u-1 for j=i+1:u %从小到大排列 if a1(i)>a1(j) b1=a1(i); a1(i)=a1(j); a1(j)=b1; end end end c1=[1:1:u]; %plot(c1,a1); b=length(a1); disp('最优点为：') maxX %输出最佳x值 disp('最优解为：') maxY %输出最佳y值 toc