1. Introduction
Ant colony algorithm is also one of optimization algorithms. Ant colony algorithm is good at solving combinatorial optimization problems. Ant colony algorithm can effectively solve the well-known traveling salesman problem (TSP), and more than that, it has also achieved certain results in other fields, such as process sequencing problems, graph coloring problems, network routing problems, and so on. Next, I will briefly introduce the basic ideas of ant colony algorithm.
Ant colony algorithm, as the name suggests, is an algorithm derived from the foraging behavior of ant colonies. The foraging behavior of a single ant seems chaotic, but according to the observation of entomologists, the ant colony can always find the closest route to the food when foraging. What is the reason? In fact, the eyesight of ants is not very good, but by what area do they find the shortest path to food? Through research, it is found that each ant releases a substance called pheromone along the way when foraging. Other ants will be aware of this substance, therefore, this substance will affect the foraging behavior of other ants. When there are more ants on some paths, the pheromone concentration on this path will be higher, and the more likely it is for other ants to choose this path, thereby increasing the pheromone concentration on this path. Of course, the concentration of pheromone on a path will also decrease over time. This selection process is called ant's autocatalytic behavior, which is a positive feedback mechanism, and the entire ant colony can also be identified as an enhanced learning system.
In order to let everyone better understand the foraging behavior of the ant colony mentioned above, here is a picture to illustrate the foraging behavior of the ant colony.
As shown in the figure, point A is an ant nest, and there are two ants in it, ant 1 and ant 2. Point B is where the food is, and point C is just a point on the path. Suppose ABC forms an equilateral triangle, and the moving speeds of two ants are the same.
At time t0, two ants are in the nest, and there are two ways to choose between them, namely AB or AC. Two ants choose randomly. We assume that ant 1 chooses path AC, and ant 2 chooses path AB.
At time t1, ant 1 walks to point C, and ant 2 walks to point B, where the food is located. They release pheromones on their path, which is indicated by dotted lines on the way. Ant 2 then transports the food to the ant nest and is still releasing pheromones along the way, while Ant 1 heads from point C to point B.
At time t2, ant 2 reached point A of ant nest, and ant 1 reached point B where the food was. At this time, ant 2 set out to carry food again. It found that the pheromone concentration on the AB path was higher than that on the AC path. Pheromone concentration (there are two dotted lines on the AB path and only one dotted line on the AC path). Therefore, Ant 2 chooses the AB path to carry food, and Ant 1 gets the food at point B and then returns to the ant nest. However, it also has two options, one is to return on the same route, and the other is to follow the route AB. Ant 1 finds that the pheromone concentration on the AB path is higher than the pheromone concentration on the AC path, so it will choose AB to return to the nest.
In this way, the pheromone concentration of the AC path will be lower and lower, and the pheromone concentration of the AB path will be higher and higher, so there will be no ants on the AC path to pass again, and both ants will only choose the shorter path AB Line to carry food.
Second, the source code
%% 清空环境
clc;clear
%% 障碍物数据
position = load('barrier.txt');
plot([0,200],[0,200],'.');
hold on
B = load('barrier.txt');
xlabel('km','fontsize',12)
ylabel('km','fontsize',12)
title('二维规划空间','fontsize',12)
%% 描述起点和终点
S = [20,180];
T = [160,90];
plot([S(1),T(1)],[S(2),T(2)],'.');
% 图形标注
text(S(1)+2,S(2),'S');
text(T(1)+2,T(2),'T');
%% 描绘障碍物图形
fill(position(1:4,1),position(1:4,2),[0,0,0]);
fill(position(5:8,1),position(5:8,2),[0,0,0]);
fill(position(9:12,1),position(9:12,2),[0,0,0]);
fill(position(13:15,1),position(13:15,2),[0,0,0]);
% 下载链路端点数据
L = load('lines.txt');
%% 描绘线及中点
v = zeros(size(L));
for i=1:20
plot([position(L(i,1),1),position(L(i,2),1)],[position(L(i,1),2)...
,position(L(i,2),2)],'color','black','LineStyle','--');
v(i,:) = (position(L(i,1),:)+position(L(i,2),:))/2;
plot(v(i,1),v(i,2),'*');
text(v(i,1)+2,v(i,2),strcat('v',num2str(i)));
end
%% 描绘可行路径
sign = load('matrix.txt');
[n,m]=size(sign);
for i=1:n
if i == 1
for k=1:m-1
if sign(i,k) == 1
plot([S(1),v(k-1,1)],[S(2),v(k-1,2)],'color',...
'black','Linewidth',2,'LineStyle','-');
end
end
continue;
end
for j=2:i
if i == m
if sign(i,j) == 1
plot([T(1),v(j-1,1)],[T(2),v(j-1,2)],'color',...
'black','Linewidth',2,'LineStyle','-');
end
else
if sign(i,j) == 1
plot([v(i-1,1),v(j-1,1)],[v(i-1,2),v(j-1,2)],...
'color','black','Linewidth',2,'LineStyle','-');
end
end
end
end
path = DijkstraPlan(position,sign);
j = path(22);
plot([T(1),v(j-1,1)],[T(2),v(j-1,2)],'color','yellow','LineWidth',3,'LineStyle','-.');
i = path(22);
j = path(i);
count = 0;
while true
plot([v(i-1,1),v(j-1,1)],[v(i-1,2),v(j-1,2)],'color','yellow','LineWidth',3,'LineStyle','-.');
count = count + 1;
i = j;
j = path(i);
if i == 1 || j==1
break;
end
end
plot([S(1),v(i-1,1)],[S(2),v(i-1,2)],'color','yellow','LineWidth',3,'LineStyle','-.');
count = count+3;
pathtemp(count) = 22;
j = 22;
for i=2:count
pathtemp(count-i+1) = path(j);
j = path(j);
end
path = pathtemp;
path = [1 9 8 7 13 14 12 22];
%% 蚁群算法参数初始化
pathCount = length(path)-2; %经过线段数量
pheCacuPara=2; %信息素计算参数
pheThres = 0.8; %信息素选择阈值
pheUpPara=[0.1 0.0003]; %信息素更新参数
qfz= zeros(pathCount,10); %启发值
phePara = ones(pathCount,10)*pheUpPara(2); %信息素
qfzPara1 = ones(10,1)*0.5; %启发信息参数
qfzPara2 = 1.1; %启发信息参数
m=10; %种群数量
NC=500; %循环次数
pathk = zeros(pathCount,m); %搜索结果记录
shortestpath = zeros(1,NC); %进化过程记录
%% 初始最短路径
dijpathlen = 0;
vv = zeros(22,2);
vv(1,:) = S;
vv(22,:) = T;
vv(2:21,:) = v;
for i=1:pathCount-1
dijpathlen = dijpathlen + sqrt((vv(path(i),1)-vv(path(i+1),1))^2+(vv(path(i),2)-vv(path(i+1),2))^2);
end
LL = dijpathlen;
figure;
plot(1:NC,shortestpath,'color','blue');
hold on
% plot(1:NC,dijpathlen,'color','red');
ylabel('路径总长度');
xlabel('迭代次数');
Three, running results
Four, remarks
Add QQ912100926 for complete code or writing.
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