哪个网站可以学做标书,网站后台管理系统查询,Wordpress炫酷特效,深圳平面设计培训目录 效果一览基本介绍程序设计参考文献 效果一览 基本介绍 基于蚁群算法的三维无人机航迹规划#xff08;Matlab#xff09;。 蚁群算法#xff08;Ant Colony Optimization#xff0c;ACO#xff09;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… 目录 效果一览基本介绍程序设计参考文献 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 OptimizationACO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
clc
clear
close all%% 输入数据
G[ 0 0 1 1 1 0 0 0 0 11 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 00 1 1 0 1 0 0 0 0 00 1 1 0 1 0 0 0 1 00 0 0 0 0 0 1 0 0 00 0 0 1 0 0 0 0 0 10 0 0 0 0 0 1 1 0 00 1 0 0 0 0 1 0 0 00 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;
dsort(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:NPfor j 1:dimcol G(:,j1); % 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XXpX;
[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 : maxgengen[ ans, sortIndex ] sort( fit );% Sort.[fmax,B]max( fit );worse X(B,:); [~, Idx] sort( fpbest );r2rand(1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i 1 : pNumif(r20.9)r1rand(1);arand(1,1);if (a0.1)a1;elsea-1;endX( i , : ) pX( i , :)0.3*abs(pX(i , : )-worse)a*0.1*(XX( i , :)); % Equation (1)elseaaa randperm(180,1);if ( aaa0 ||aaa90 ||aaa180 )X( i , : ) pX( i , :); endtheta aaa*pi/180; X( i , : ) pX( i , :)tan(theta).*abs(pX(i , : )-XX( i , :)); % Equation (2) endX( i , :) Bounds(X(i , : ), Xmin, Xmax );fit( i ) fitness( X( i , : ),G );end [ fMMin, bestII ] min( fit ); bestXX X( bestII, : );
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%R1-gen/maxgen; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Xnew1 bestXX.*(1-R); Xnew2 bestXX.*(1R); %%% Equation (3)Xnew1 Bounds(Xnew1, Xmin, Xmax );Xnew2 Bounds(Xnew2, Xmin, Xmax );%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Xnew11 bestX.*(1-R); Xnew22 bestX.*(1R); %%% 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 );endfor 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 );endfor j 20 : NP % Equation (7)X( j,: )bestXrandn(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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% XXpX;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 更新个体最优值和全局最优值for i 1 : NPif (fit(i) fpbest(i))fpbest(i) fit(i);pX(i, :) X(i, :);endif(fpbest(i) fgbest)fgbest fpbest(i);bestX pX(i, :);endendbestX LocalSearch(bestX,Xmax,G);fgbest fitness(bestX,G);FG(gen,1)fgbest;
end参考文献 [1] 基于人工势场结合快速搜索树APFRRT实现机器人避障规划附matlab代码 [2] 基于蚁群算法求解栅格地图路径规划问题matlab源码含GUI
