蚁群算法路径规划MATLAB程序

x=ROUT(s);
y=ROUT(s+1);
Delta_Tau(x,y)=Delta_Tau(x,y)+Q/PL_km;
Delta_Tau(y,x)=Delta_Tau(y,x)+Q/PL_km;
% passes
% Tau output updated pheromone
%% -------------------- variableinitialization-----------------------------
D=G2D(G);
N=size(D,1);%N is the peoblem scale(pixel number)
PLkm=PLkm+DD(W,to_visit);%Path length accumulation
W=to_visit;%move to the next point
for kk=1:N
if TABUkm(kk)==0
if i~=E
Eta(1,i)=1/((ix-Ex)^2+(iy-Ey)^2)^0.5;
else
Eta(1,i)=100;
end
end
ROUTES=cell(K,M);% use cell construct to load the passing route of every ant for every iteration
Len_LJD=length(LJD);%number of points can be selected
%% Colony stop condition：there is no food or go into a impass
while W~=E&&Len_LJD>=1
%% ---------------------------------------------------------------
% input parameters
% G gragh, which is 01 matrix, 1 represents obstcles
function [ROUTES,PL,Tau]=ACASP(G,Tau,K,M,S,E,Alpha,Beta,Rho,Q)
%% ---------------------------------------------------------------
% ACASP.m
% ACS for Path Planning
end
end
end
Tau=(1-Rho).*Tau+Delta_Tau;%pheromone evaporates some and accumulates some
end
%% ---------------------------PLOT--------------------------------
DW=DD(W,:);
DW1=find(DW<inf);
for j=1:length(DW1)
if TABUkm(DW1(j))==0
DW(j)=inf;
PLK=PL(i,:);
Nonzero=find(PLK<inf);
PLKPLK=PLK(Nonzero);
meanPL(i)=mean(PLKPLK);
minPL(i)=min(PLKPLK);
end
for j=1:length(DW1)
if TABUkm(DW1(j))==0
DW(j)=inf;
end
end
LJD=find(DW<inf);% set of points can be selected
% Alpha parameter indicates the importance of pheromone
% Beta parameter indicates the importance of visibility
% Rho pheromone evaporation rate
end
end
LJD=find(DW<inf);%set of optional points
Len_LJD=length(LJD);%number of optional points
end
%% Fifth Step：record the pate and length that every ant for every
%% iteration passes
ROUTES{k,m}=Path;
if Path(end)==E
plotif=1;%control parameter, determine if plot or not
if plotif==1
%plot convergence curve
meanPL=zeros(1,K);
minPL=zeros(1,K);
for i=1:K
end
PP=PP/(sum(PP));%construct probability distributing
Pcum=cumsum(PP);
Select=find(Pcum>=rand);
to_visit=LJD(Select(1));%the next step node
%% Fourth Step：status update and record
Path=[Path,to_visit];%Paths accumulation
% Q pheromone accumulation parameter
%
% output parameters
% ROUTES the route that every ant in every iteration
% passes
% PL the length that every ant in every iteration
PLkm=0;%initialize the passing length
TABUkm=ones(1,N);%initialize the forbbiden table
TABUkm(S)=0;% eliminate this point for it is the starting point
MM=size(G,1);
a=1;% edge length of pixel
Ex=a*(mod(E,MM)-0.5);% goal point coordinate
if Ex==-0.5
Ex=MM-0.5;
end
Ey=a*(MM+0.5-ceil(E/MM));% Y coordinate of goal point
DD(W,kk)=inf;
DD(kk,W)=inf;
end
end
TABUkm(W)=0;%eliminate the travelled point in the forbidden table
%% Third Step：the method to choose the next step
PP=zeros(1,Len_LJD);
for i=1:Len_LJD
PP(i)=(Tau(W,LJD(i))^Alpha)*(Eta(LJD(i))^Beta);
DD=D;%initialize adjacency matrix
%% Second Step：Choose the nodes that can step forward
DW=DD(W,:);
DW1=find(DW<inf);
figure(1)
plot(minPL);
hold on
plot(meanPL);
PL=zeros(K,M);% use matrix to load the passing length of every ant for every iteration
%% -----------iteration time is k, ant number is M--------------------
PL(k,m)=PLkm;
else
PL(k,m)=inf;
end
end
%% Sixth Step: update pheromone
Delta_Tau=zeros(N,N);%initialize updating ammount
for m=1:M
if PL(k,m)<inf
ROUT=ROUTES{k,m}; TS=length(Fra bibliotekOUT)-1;
PL_km=PL(k,m);
for s=1:TS
% Tau initial pheromone matrix
% K iterative times
% M ant number for once iteration
% S Start Point
% E Goal point
for k=1:K
disp(k);
for m=1:M
%% First step: status initialization
W=S;%set current point as the starting point
Path=S;%initialize the passing route
Eta=zeros(1,N);
%construct the initialization matrix