matlab-(含教程)基于ACO蚁群优化算法的智能leach多径动态优化matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); model=CreateModel(); CostFunction=@(tour) TourLength(tour,model); nVar=model.n; % ACO Parameters MaxIt=100; % Maximum Number of Iterations nAnt=40; % Number of Ants (Population Size) Q=1; tau0=10*Q/(nVar*mean(model.D(:))); % Initial Phromone alpha=1; % Phromone Exponential Weight beta=1; % Heuristic Exponential Weight rho=0.05; % Evaporation Rate % Initialization 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; 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 figure; plot(BestCost,'LineWidth',2); xlabel('Iteration'); ylabel('Best Cost'); grid on;