Initial Release.
Project description
This code is to solve traveling salesman problem by using simulated annealing meta heuristic.
```
import numpy
import pytspsa
solver = pytspsa.Tsp_sa()
c = [
[0, 0],
[0, 1],
[0, 2],
[0, 3]
]
c = numpy.asarray(c, dtype=numpy.float32)
solver.set_num_nodes(4)
solver.add_by_coordinates(c)
solver.set_t_v_factor(4.0)
# solver.sa() or sa_auto_parameter() will solve the problem.
solver.sa_auto_parameter(12)
# getting result
solution = solver.getBestSolution()
print('Length={}'.format(solution.getlength()))
print('Path= {}'.format(solution.getRoute()))
```
See github page.
```
import numpy
import pytspsa
solver = pytspsa.Tsp_sa()
c = [
[0, 0],
[0, 1],
[0, 2],
[0, 3]
]
c = numpy.asarray(c, dtype=numpy.float32)
solver.set_num_nodes(4)
solver.add_by_coordinates(c)
solver.set_t_v_factor(4.0)
# solver.sa() or sa_auto_parameter() will solve the problem.
solver.sa_auto_parameter(12)
# getting result
solution = solver.getBestSolution()
print('Length={}'.format(solution.getlength()))
print('Path= {}'.format(solution.getRoute()))
```
See github page.
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