AS_蚁群算法求解TSP问题matlab_源码

%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