遗传算法路径规划

function [L1,XY1,L2,XY2]=yichuandaima(XX,YY) %% 基于Dijkstra和遗传算法的机器人路径规划演示程序 % GreenSim团队原创作品，转载请注明 % GreenSim团队长期从事算法设计、代写程序等业务 % 欢迎访问GreenSim——算法仿真团队→http://blog.sina.com.cn/greensim %输入参数在函数体内部定义 %输出参数为 % L1 由Dijkstra算法得出的最短路径长度 % XY1 由Dijkstra算法得出的最短路径经过节点的坐标 % L2 由遗传算法得出的最短路径长度 % XY2 由遗传算法得出的最短路径经过节点的坐标 %程序输出的图片有 % Fig1 环境地图（包括：边界、障碍物、障碍物顶点之间的连线、Dijkstra的网络图结构） % Fig2 由Dijkstra算法得到的最短路径 % Fig3 由遗传算法得到的最短路径 % Fig4 遗传算法的收敛曲线（迄今为止找到的最优解、种群平均适应值） %% 画Fig1 figure(1); PlotGraph; title('地形图及网络拓扑结构') PD=inf*ones(26,26); for i=1:26 for j=1:26 if D(i,j)==1 x1=XY(i,5); y1=XY(i,6); x2=XY(j,5); y2=XY(j,6); dist=((x1-x2)^2+(y1-y2)^2)^0.5; PD(i,j)=dist; end end end %% 调用最短路算法求最短路 s=1;%出发点 t=26;%目标点 [L,R]=ZuiDuanLu(PD,s,t); L1=L(end); XY1=XY(R,5:6); %% 绘制由最短路算法得到的最短路径 figure(2); PlotGraph; hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('由Dijkstra算法得到的初始路径') %% 使用遗传算法进一步寻找最短路 %第一步：变量初始化 M=50;%进化代数设置 N=20;%种群规模设置 Pm=0.3;%变异概率设置 LC1=zeros(1,M); LC2=zeros(1,M); Yp=L1; %第二步：随机产生初始种群 X1=XY(R,1); Y1=XY(R,2); X2=XY(R,3); Y2=XY(R,4); for i=1:N farm{i}=rand(1,aaa); end % 以下是进化迭代过程 counter=0;%设置迭代计数器 while counter<M%停止条件为达到最大迭代次数 %% 第三步：交叉 %交叉采用双亲双子单点交叉 newfarm=cell(1,2*N);%用于存储子代的细胞结构 Ser=randperm(N);%两两随机配对的配对表 A=farm{Ser(1)};%取出父代A B=farm{Ser(2)};%取出父代B P0=unidrnd(aaa-1);%随机选择交叉点 a=[A(:,1:P0),B(:,(P0+1):end)];%产生子代a b=[B(:,1:P0),A(:,(P0+1):end)];%产生子代b newfarm{2*N-1}=a;%加入子代种群 newfarm{2*N}=b; for i=1:(N-1) A=farm{Ser(i)}; B=farm{Ser(i+1)}; newfarm{2*i}=b; end FARM=[farm,newfarm];%新旧种群合并 %% 第四步：选择复制 SER=randperm(2*N); FITNESS=zeros(1,2*N); fitness=zeros(1,N); for i=1:(2*N) PP=FARM{i}; FITNESS(i)=MinFun(PP,X1,X2,Y1,Y2);%调用目标函数 end for i=1:N f1=FITNESS(SER(2*i-1)); f2=FITNESS(SER(2*i)); if f1<=f2 else farm{i}=FARM{SER(2*i)}; fitness(i)=FITNESS(SER(2*i)); end end %记录最佳个体和收敛曲线 minfitness=min(fitness); meanfitness=mean(fitness); if minfitness <Yp pos=find(fitness==minfitness); Xp=farm{pos(1)}; Yp=minfitness; end if counter==10 PPP=[0.5,Xp,0.5]'; PPPP=1-PPP; X=PPP.*X1+PPPP.*X2; Y=PPP.*Y1+PPPP.*Y2; XY2=[X,Y]; figure(3) PlotGraph; hold on for i=1:(length(R)-1) x1=XY2(i,1); y1=XY2(i,2); x2=XY2(i+1,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('遗传算法第10代') hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end end if counter==20 PPP=[0.5,Xp,0.5]'; PPPP=1-PPP; X=PPP.*X1+PPPP.*X2; Y=PPP.*Y1+PPPP.*Y2; XY2=[X,Y]; figure(4) PlotGraph; hold on for i=1:(length(R)-1) x1=XY2(i,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('遗传算法第20代') hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end end if counter==30 PPP=[0.5,Xp,0.5]'; PPPP=1-PPP; X=PPP.*X1+PPPP.*X2; Y=PPP.*Y1+PPPP.*Y2; XY2=[X,Y]; figure(5) PlotGraph; hold on for i=1:(length(R)-1) x1=XY2(i,1); y1=XY2(i,2); x2=XY2(i+1,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('遗传算法第30代') hold on for i=1:(length(R)-1) x1=XY1(i,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end end if counter==40 PPP=[0.5,Xp,0.5]'; PPPP=1-PPP; X=PPP.*X1+PPPP.*X2; Y=PPP.*Y1+PPPP.*Y2; XY2=[X,Y]; figure(6) PlotGraph; hold on for i=1:(length(R)-1) x1=XY2(i,1); y1=XY2(i,2); x2=XY2(i+1,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('遗传算法第40代') hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end end if counter==50 PPP=[0.5,Xp,0.5]'; PPPP=1-PPP; X=PPP.*X1+PPPP.*X2; Y=PPP.*Y1+PPPP.*Y2; XY2=[X,Y]; figure(7) PlotGraph; hold on for i=1:(length(R)-1) x1=XY2(i,1); y1=XY2(i,2); x2=XY2(i+1,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('遗传算法第50代') hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end end LC2(counter+1)=Yp; LC1(counter+1)=meanfitness; %% 第五步：变异 for i=1:N if Pm>rand&&pos(1)~=i AA=farm{i}; AA(POS)=rand; farm{i}=AA; end end counter=counter+1; disp(counter); end %% 输出遗传算法的优化结果 PPP=[0.5,Xp,0.5]'; PPPP=1-PPP; X=PPP.*X1+PPPP.*X2; Y=PPP.*Y1+PPPP.*Y2; XY2=[X,Y]; L2=Yp; %% 绘制Fig3 figure(8) PlotGraph; hold on hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end for i=1:(length(R)-1) x1=XY2(i,1); y1=XY2(i,2); x2=XY2(i+1,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k'); hold on end title('遗传算法最终结果') figure(9) PlotGraph; hold on for i=1:(length(R)-1) x1=XY1(i,1); y1=XY1(i,2); x2=XY1(i+1,1); y2=XY1(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',1); hold on end hold on for i=1:(length(R)-1) x1=XY2(i,1); y1=XY2(i,2); x2=XY2(i+1,1); y2=XY2(i+1,2); plot([x1,x2],[y1,y2],'k','LineWidth',2); hold on end title('遗传算法优化前后结果比较') %% 绘制Fig4 figure(10); plot(LC1); hold on plot(LC2); xlabel('迭代次数'); title('收敛曲线');