11基于遗传算法的机器人路径规划MATLAB源代码

欢迎访问GreenSim团队主页→http://blog.sina.com.cn/greensim 邮箱：greensim@163.com 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

第6页

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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);

第7页

欢迎访问GreenSim团队主页→http://blog.sina.com.cn/greensim 邮箱：greensim@163.com 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');

源代码运行结果展示

第8页

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第9页