%反向前程序
clc;
clear;
close all;
%% Problem Definition
model=CreateModel();
CostFunction=@(tour) TourLength(tour,model);
nVar=model.n;
%% ACO Parameters
MaxIt=2000; % Maximum Number of Iterations
nAnt=50; % Number of Ants (Population Size)
Q=1;
tau0=10*Q/(nVar*mean(model.D(:))); % Initial Phromone
alpha=1; % Phromone Exponential Weight
beta=2; % Heuristic Exponential Weight
rho=0.05; % Evaporation Rate
N_max=1;
t1=clock;
%% Initialization
for kk=1:N_max
BestSol.Cost=inf;
eta=1./model.D; % Heuristic Information Matrix
tau=tau0*ones(nVar,nVar); % Phromone Matrix
BestCost=zeros(MaxIt,1); % Array to Hold Best Cost Values
% Empty Ant
empty_ant.Tour=[];
empty_ant.Cost=[];
% Ant Colony Matrix
ant=repmat(empty_ant,nAnt,1);
% Best Ant
% BestSol.Cost=inf;
%% ACO Main Loop
for it=1:MaxIt
% Move Ants
for k=1:nAnt
ant(k).Tour=randi([1 nVar]);
for l=2:nVar
i=ant(k).Tour(end);
P=tau(i,:).^alpha.*eta(i,:).^beta;
P(ant(k).Tour)=0;
P=P/sum(P);
j=RouletteWheelSelection(P);
ant(k).Tour=[ant(k).Tour j];
end
ant(k).Cost=CostFunction(ant(k).Tour);
if ant(k).Cost<BestSol.Cost
BestSol=ant(k);
end
end
% Update Phromones
for k=1:nAnt
tour=ant(k).Tour;
tour=[tour tour(1)]; %#ok
for l=1:nVar
i=tour(l);
j=tour(l+1);
tau(i,j)=tau(i,j)+(Q/ant(k).Cost)/(1-rho);
end
end
% Evaporation
tau=(1-rho)*tau;
% Store Best Cost
BestCost(it)=BestSol.Cost;
% Show Iteration Information
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
% Plot Solution
figure(1);
PlotSolution(BestSol.Tour,model);
pause(0.01);
end
%% Results
figure;
plot(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;
bestcost_global(kk)=BestCost(MaxIt);
all_BestCost(:,kk) = BestCost;
kk
end
maxcost_global=max(bestcost_global)
mincost_global=min(bestcost_global)
meancost_global=mean(bestcost_global)
stdcost_global=std(bestcost_global)
t2=clock;
time = etime(t2,t1)
save
