目录
效果一览
基本介绍
基于蚁群算法的三维无人机航迹规划（Matlab）。
蚁群算法（Ant Colony Optimization，ACO）是一种模拟蚂蚁觅食行为的启发式算法。该算法通过模拟蚂蚁在寻找食物时的行为，来解决各种优化问题，尤其是在图论和组合优化方面应用较广。
程序设计
- 完整源码和数据私信博主回复基于蚁群算法的三维无人机航迹规划（Matlab）
matlab
clc
clear
close all
%% 输入数据
G=[ 0 0 1 1 1 0 0 0 0 1
1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 1 1 0 1 0 0 0 0 0
0 1 1 0 1 0 0 0 1 0
0 0 0 0 0 0 1 0 0 0
0 0 0 1 0 0 0 0 0 1
0 0 0 0 0 0 1 1 0 0
0 1 0 0 0 0 1 0 0 0
0 1 0 0 1 0 0 0 0 0];
% G=[ 0 1 1 1 0
% 1 0 0 0 0
% 0 0 0 0 1
% 0 0 0 0 1
% 0 1 1 0 1];
G = zeros(10,10);
d = randperm(95,21)+1;
d=sort(d);
G(d) = 1;
%% 栅格绘制
drawShanGe(G,0)
title('栅格地图')
%%
S = [1 1]; % 起点
E = [10 10]; % 终点
G0 = G;
G = G0(S(1):E(1),S(2):E(2)); % 该方式是为了方便更改起点与终点
[Xmax,dim] = size(G); % 栅格地图列数为粒子维数，行数为粒子的变化范围
dim = dim - 2; % 减2是去掉起点与终点
%% 参数设置
maxgen = 50; % 最大迭代次数
NP = 30; % 种群数量
%%%%%%%%%%%%%%%%%%%%%%%%%%%
rPercent = 0.2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%
pNum = round( NP * rPercent ); % %发现者
Xmin = 1; % 变量下界
%% 初始化
X = zeros(NP,dim);
for i = 1:NP
for j = 1:dim
col = G(:,j+1); % 地图的一列
id = find(col == 0); % 该列自由栅格的位置
X(i,j) = id(randi(length(id))); % 随机选择一个自由栅格
id = [];
end
fit( i ) = fitness(X( i, : ),G);
end
fpbest = fit; % 个体最优适应度
pX = X; % 个体最优位置
XX=pX;
[fgbest, bestIndex] = min( fit ); % 全局最优适应度
bestX = X(bestIndex, : ); % 全局最优位置
[fmax,B]=max(fit);
worse= X(B,:);
%%
for gen = 1 : maxgen
gen
[ ans, sortIndex ] = sort( fit );% Sort.
[fmax,B]=max( fit );
worse= X(B,:);
[~, Idx] = sort( fpbest );
r2=rand(1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i = 1 : pNum
if(r2<0.9)
r1=rand(1);
a=rand(1,1);
if (a>0.1)
a=1;
else
a=-1;
end
X( i , : ) = pX( i , :)+0.3*abs(pX(i , : )-worse)+a*0.1*(XX( i , :)); % Equation (1)
else
aaa= randperm(180,1);
if ( aaa==0 ||aaa==90 ||aaa==180 )
X( i , : ) = pX( i , :);
end
theta= aaa*pi/180;
X( i , : ) = pX( i , :)+tan(theta).*abs(pX(i , : )-XX( i , :)); % Equation (2)
end
X( i , :) = Bounds(X(i , : ), Xmin, Xmax );
fit( i ) = fitness( X( i , : ),G );
end
[ fMMin, bestII ] = min( fit );
bestXX = X( bestII, : );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
R=1-gen/maxgen; %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Xnew1 = bestXX.*(1-R);
Xnew2 =bestXX.*(1+R); %%% Equation (3)
Xnew1= Bounds(Xnew1, Xmin, Xmax );
Xnew2 = Bounds(Xnew2, Xmin, Xmax );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Xnew11 = bestX.*(1-R);
Xnew22 =bestX.*(1+R); %%% Equation (5)
Xnew11= Bounds(Xnew11, Xmin, Xmax );
Xnew22 = Bounds(Xnew22, Xmin, Xmax );
for i = ( pNum + 1 ) :12 % Equation (4)
X( i, : )=bestXX+((rand(1,dim)).*(pX( i , : )-Xnew1)+(rand(1,dim)).*(pX( i , : )-Xnew2));
X( i , :) = Bounds(X(i , : ), min(Xnew1), max(Xnew2) );
fit( i ) = fitness( X( i , : ),G );
end
for i = 13: 19 % Equation (6)
X( i, : )=pX( i , : )+((randn(1)).*(pX( i , : )-Xnew11)+((rand(1,dim)).*(pX( i , : )-Xnew22)));
X( i , :) = Bounds(X(i , : ), Xmin, Xmax );
fit( i ) = fitness( X( i , : ),G );
end
for j = 20 : NP % Equation (7)
X( j,: )=bestX+randn(1,dim).*((abs(( pX(j,: )-bestXX)))+(abs(( pX(j,: )-bestX))))./2;
X( j , :) = Bounds(X(j , : ), Xmin, Xmax );
fit( j ) = fitness( X( j , : ),G );
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
XX=pX;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 更新个体最优值和全局最优值
for i = 1 : NP
if (fit(i) < fpbest(i))
fpbest(i) = fit(i);
pX(i, :) = X(i, :);
end
if(fpbest(i) < fgbest)
fgbest = fpbest(i);
bestX = pX(i, :);
end
end
bestX = LocalSearch(bestX,Xmax,G);
fgbest = fitness(bestX,G);
FG(gen,1)=fgbest;
end