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Traveling Salesman Problem

The TSP (Traveling Salesman Problem) is the classic combinatorial optimization problem. I have provided a very simple version of it, based on the coordinates of twelve cities in the southwestern United States. This should maybe be called the Flying Salesman Problem, since I am using the great-circle distance between cities, rather than the driving distance. Also: I assume the earth is a sphere, so I don't use geoid distances.

The gsl_siman_solve() routine finds a route which is 3490.62 Kilometers long; this is confirmed by an exhaustive search of all possible routes with the same initial city.

The full code can be found in siman/siman_tsp.c, but I include here some plots generated in the following way:

     $ ./siman_tsp > tsp.output
     $ grep -v "^#" tsp.output
      | xyplot -xyil -d "x................y"
         -lx "generation" -ly "distance"
         -lt "TSP -- 12 southwest cities"
      | xyps -d > 12-cities.eps
     $ grep initial_city_coord tsp.output
      | awk '{print $2, $3, $4, $5}'
      | xyplot -xyil -lb0 -cs 0.8
         -lx "longitude (- means west)" -ly "latitude"
         -lt "TSP -- initial-order"
      | xyps -d > initial-route.eps
     $ grep final_city_coord tsp.output
      | awk '{print $2, $3, $4, $5}'
      | xyplot -xyil -lb0 -cs 0.8
         -lx "longitude (- means west)" -ly "latitude"
         -lt "TSP -- final-order"
      | xyps -d > final-route.eps
     

This is the output showing the initial order of the cities; longitude is negative, since it is west and I want the plot to look like a map.

     # initial coordinates of cities (longitude and latitude)
     ###initial_city_coord: -105.95 35.68 Santa Fe
     ###initial_city_coord: -112.07 33.54 Phoenix
     ###initial_city_coord: -106.62 35.12 Albuquerque
     ###initial_city_coord: -103.2 34.41 Clovis
     ###initial_city_coord: -107.87 37.29 Durango
     ###initial_city_coord: -96.77 32.79 Dallas
     ###initial_city_coord: -105.92 35.77 Tesuque
     ###initial_city_coord: -107.84 35.15 Grants
     ###initial_city_coord: -106.28 35.89 Los Alamos
     ###initial_city_coord: -106.76 32.34 Las Cruces
     ###initial_city_coord: -108.58 37.35 Cortez
     ###initial_city_coord: -108.74 35.52 Gallup
     ###initial_city_coord: -105.95 35.68 Santa Fe
     

The optimal route turns out to be:

     # final coordinates of cities (longitude and latitude)
     ###final_city_coord: -105.95 35.68 Santa Fe
     ###final_city_coord: -106.28 35.89 Los Alamos
     ###final_city_coord: -106.62 35.12 Albuquerque
     ###final_city_coord: -107.84 35.15 Grants
     ###final_city_coord: -107.87 37.29 Durango
     ###final_city_coord: -108.58 37.35 Cortez
     ###final_city_coord: -108.74 35.52 Gallup
     ###final_city_coord: -112.07 33.54 Phoenix
     ###final_city_coord: -106.76 32.34 Las Cruces
     ###final_city_coord: -96.77 32.79 Dallas
     ###final_city_coord: -103.2 34.41 Clovis
     ###final_city_coord: -105.92 35.77 Tesuque
     ###final_city_coord: -105.95 35.68 Santa Fe
     

Here's a plot of the cost function (energy) versus generation (point in the calculation at which a new temperature is set) for this problem:

References and Further Reading

Further information is available in the following book,