%% 该函数用于演示基于蚁群算法的三维路径规划算法
%% 清空环境
clc
clear
%% 数据初始化
%下载数据
load HeightData HeightData
%网格划分
LevelGrid=10;
PortGrid=21;
%起点终点网格点
starty=10;starth=4;
endy=8;endh=6;
% endy=8;endh=5;
m=1;
%算法参数
PopNumber=10; %种群个数
BestFitness=[]; %最佳个体
%初始信息素
pheromone=ones(21,21,21);
%% 初始搜索路径
[path,pheromone]=searchpath(PopNumber,LevelGrid,PortGrid,pheromone, ...
HeightData,starty,starth,endy,endh);
fitness=CacuFit(path); %适应度计算
[bestfitness,bestindex]=min(fitness); %最佳适应度
bestpath=path(bestindex,:); %最佳路径
BestFitness=[BestFitness;bestfitness]; %适应度值记录
%% 信息素更新
rou=0.2;
cfit=100/bestfitness;
for i=2:PortGrid-1
pheromone(i,bestpath(i*2-1),bestpath(i*2))= ...
(1-rou)*pheromone(i,bestpath(i*2-1),bestpath(i*2))+rou*cfit;
end
%% 循环寻找最优路径
for kk=1:300
%% 路径搜索
[path,pheromone]=searchpath(PopNumber,LevelGrid,PortGrid,...
pheromone,HeightData,starty,starth,endy,endh);
%% 适应度值计算更新
fitness=CacuFit(path);
[newbestfitness,newbestindex]=min(fitness);
if newbestfitness<bestfitness
bestfitness=newbestfitness;
bestpath=path(newbestindex,:);
end
BestFitness=[BestFitness;bestfitness];
%% 更新信息素
cfit=100/bestfitness;
for i=2:PortGrid-1
pheromone(i,bestpath(i*2-1),bestpath(i*2))=(1-rou)* ...
pheromone(i,bestpath(i*2-1),bestpath(i*2))+rou*cfit;
end
end
%% 最佳路径
for i=1:21
a(i,1)=bestpath(i*2-1);
a(i,2)=bestpath(i*2);
end
figure(1)
x=1:21;
y=1:21;
[x1,y1]=meshgrid(x,y);
mesh(x1,y1,HeightData)
axis([1,21,1,21,0,2000])
hold on
k=1:21;
plot3(k(1)',a(1,1)',a(1,2)'*200,'--o','LineWidth',2,...
'MarkerEdgeColor','k',...
'MarkerFaceColor','g',...
'MarkerSize',10)
plot3(k(21)',a(21,1)',a(21,2)'*200,'--o','LineWidth',2,...
'MarkerEdgeColor','k',...
'MarkerFaceColor','g',...
'MarkerSize',10)
text(k(1)',a(1,1)',a(1,2)'*200,'S');
text(k(21)',a(21,1)',a(21,2)'*200,'T');
xlabel('km','fontsize',12);
ylabel('km','fontsize',12);
zlabel('m','fontsize',12);
title('三维路径规划空间','fontsize',12)
set(gcf, 'Renderer', 'ZBuffer')
hold on
plot3(k',a(:,1)',a(:,2)'*200,'--o')
%% 适应度变化
figure(2)
plot(BestFitness)
title('最佳个体适应度变化趋势')
xlabel('迭代次数')
ylabel('适应度值')
