function out = tsp(loc)
% TSP Traveling salesman problem (TSP) using SA (simulated annealing).
% TSP by itself will generate 20 cities within a unit cube and
% then use SA to slove this problem.
% TSP(LOC) solve the traveling salesman problem with cities'
% coordinates given by LOC, which is an M by 2 matrix and M is
% the number of cities.
%% For example:
%% loc = rand(50, 2);
% tsp(loc);
if nargin == 0,
% The following data is from the post by Jennifer Myers (jmyers@nwu.edu)
% to comp.ai.neural-nets. It's obtained from the figure in
% Hopfield & Tank's 1985 paper in Biological Cybernetics
% (Vol 52, pp. 141-152).
loc = [0.3663, 0.9076; 0.7459, 0.8713; 0.4521, 0.8465;
0.7624, 0.7459; 0.7096, 0.7228; 0.0710, 0.7426;
0.4224, 0.7129; 0.5908, 0.6931; 0.3201, 0.6403;
0.5974, 0.6436; 0.3630, 0.5908; 0.6700, 0.5908;
0.6172, 0.5495; 0.6667, 0.5446; 0.1980, 0.4686;
0.3498, 0.4488; 0.2673, 0.4274; 0.9439, 0.4208;
0.8218, 0.3795; 0.3729, 0.2690; 0.6073, 0.2640;
0.4158, 0.2475; 0.5990, 0.2261; 0.3927, 0.1947;
0.5347, 0.1898; 0.3960, 0.1320; 0.6287, 0.0842;
0.5000, 0.0396; 0.9802, 0.0182; 0.6832, 0.8515];
end
% Number of cities
NumCity = length(loc);
% Initialize a distance matrix
distance = zeros(NumCity);
% Fill the distance matrix 给定距离矩阵
for i = 1:NumCity,
for j = 1:NumCity,
%distance(i, j) = (norm(loc(i, :)),norm(loc(j, :)));
distance(i, j) = norm(loc(i,loc(j,: )));
end
end
% To generate energy (objective function) from path（路径）
%path = randperm(NumCity);
%energy = sum(distance((path-1)*NumCity + [path(2:NumCity) path(1)]));
% Find typical values of dE
count = 20;
all_dE = zeros(count, 1); %初始化 0 距阵
for i = 1:count %进行 count 次跌代
评论0