基于蚁群算法的带有障碍物的TSP旅行商问题matlab代码

Sure, here's an example of a MATLAB code for solving the TSP (Traveling Salesman Problem) with obstacles using the Ant Colony Optimization (ACO) algorithm:

% TSP with obstacles using Ant Colony Optimization algorithm
% Assume the coordinates of cities are stored in the 'cities' matrix
% Assume the coordinates of obstacles are stored in the 'obstacles' matrix
% 'nCities' represents the number of cities; 'nAnts' represents the number of ants

nIterations = 100; % Number of iterations
nAnts = 20; % Number of ants
nCities = size(cities, 1); % Number of cities
alpha = 1; % Parameter controlling the influence of pheromone
beta = 5; % Parameter controlling the influence of distance
rho = 0.5; % Evaporation rate
Q = 100; % Pheromone deposit factor

% Calculate the distances between cities and obstacles
distances = zeros(nCities, nCities);
for i = 1:nCities
    for j = 1:nCities
        distances(i, j) = norm(cities(i, :) - cities(j, :));
    end
end

% Initialize pheromone levels on edges and visibility matrix
pheromones = ones(nCities, nCities);
visibility = 1 ./ distances;

% Initialize best path and its length
bestPath = [];
bestPathLength = inf;

for iteration = 1:nIterations
    % Initialize ant paths and visited cities
    paths = zeros(nAnts, nCities);
    visited = zeros(nAnts, nCities);
    
    for ant = 1:nAnts
        currentCity = randi(nCities); % Choose a random starting city
        
        for step = 1:nCities-1
            visited(ant, currentCity) = 1;
            availableCities = find(visited(ant, :) == 0); % Get unvisited cities
            
            % Calculate the probabilities for selecting the next city
            probabilities = zeros(1, length(availableCities));
            for i = 1:length(availableCities)
                city = availableCities(i);
                probabilities(i) = (pheromones(currentCity, city) ^ alpha) * (visibility(currentCity, city) ^ beta);
            end
            
            sumProbabilities = sum(probabilities);
            probabilities = probabilities / sumProbabilities;
            
            % Roulette wheel selection
            rouletteWheel = cumsum(probabilities);
            randomValue = rand();
            nextCityIndex = find(rouletteWheel >= randomValue, 1);
            nextCity = availableCities(nextCityIndex);
            
            paths(ant, step) = nextCity;
            currentCity = nextCity;
        end
        
        % Complete the path by returning to the starting city
        remainingCities = find(visited(ant, :) == 0);
        paths(ant, end) = remainingCities(1);
        
        % Evaluate the path length
        pathLength = 0;
        for i = 1:nCities-1
            pathLength = pathLength + distances(paths(ant, i), paths(ant, i+1));
        end
        
        % Update the best path if a shorter path is found
        if pathLength < bestPathLength
            bestPathLength = pathLength;
            bestPath = paths(ant, :);
        end
    end
    
    % Update pheromone levels
    pheromones = (1 - rho) * pheromones; % Evaporation
    for ant = 1:nAnts
        for i = 1:nCities-1
            pheromones(paths(ant, i), paths(ant, i+1)) = pheromones(paths(ant, i), paths(ant, i+1)) + Q / bestPathLength;
        end
    end
end

% Plot the best path
figure;
plot(cities(:, 1), cities(:, 2), 'ro'); % Plot cities as red circles
hold on;
plot(cities(bestPath, 1), cities(bestPath, 2), 'b-'); % Plot best path as blue line
plot(obstacles(:, 1), obstacles(:, 2), 'kx'); % Plot obstacles as black crosses
title('TSP with Obstacles');
xlabel('X');
ylabel('Y');
legend('Cities', 'Best Path', 'Obstacles');

Please note that you would need to provide the coordinates of cities and obstacles in their respective matrices (`cities` and `obstacles`). Additionally, you can adjust the parameters (`nIterations`, `nAnts`, `alpha`, `beta`, `rho`, `Q`) based on your requirements.

I hope this helps! Let me know if you have any further questions.