| Description |
TSPO_GA Open Traveling Salesman Problem (TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to a variation of the TSP by setting up a GA to search for the shortest route (least distance for the salesman to travel to each city exactly once without returning to the starting city)
Summary:
1. A single salesman travels to each of the cities but does not close the loop by returning to the city he started from
2. Each city is visited by the salesman exactly once
Input:
XY (float) is an Nx2 (or Nx3) matrix of cities
DMAT (float) is an NxN matrix of point to point distances/costs
POPSIZE (scalar integer) is the size of the population (should be divisible by 4)
NUMITER (scalar integer) is the number of desired iterations for the algorithm to run
SHOWPROG (scalar logical) shows the GA progress if true
SHOWRESULT (scalar logical) shows the GA results if true
Output:
OPTRTE (integer array) is the best route found by the algorithm
MINDIST (scalar float) is the cost of the best route
Example:
n = 50;
xy = 10*rand(n,2);
popSize = 60;
numIter = 1e4;
showProg = 1;
showResult = 1;
a = meshgrid(1:n);
dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),n,n);
[optRoute,minDist] = tspo_ga(xy,dmat,popSize,numIter,showProg,showResult);
Example:
n = 50;
phi = (sqrt(5)-1)/2;
theta = 2*pi*phi*(0:n-1);
rho = (1:n).^phi;
[x,y] = pol2cart(theta(:),rho(:));
xy = 10*([x y]-min([x;y]))/(max([x;y])-min([x;y]));
popSize = 60;
numIter = 1e4;
showProg = 1;
showResult = 1;
a = meshgrid(1:n);
dmat = reshape(sqrt(sum((xy(a,:)-xy(a',:)).^2,2)),n,n);
[optRoute,minDist] = tspo_ga(xy,dmat,popSize,numIter,showProg,showResult);
Example:
n = 50;
xyz = 10*rand(n,3);
popSize = 60;
numIter = 1e4;
showProg = 1;
showResult = 1;
a = meshgrid(1:n);
dmat = reshape(sqrt(sum((xyz(a,:)-xyz(a',:)).^2,2)),n,n);
[optRoute,minDist] = tspo_ga(xyz,dmat,popSize,numIter,showProg,showResult); |
