def get_solution(self):
self.display_method = "Simulated annealing"
self.display_parameters = {
'initial_temp': self.initial_temp,
'decrement': self.decrement
}
return TSPSolver.get_solution(self)
def get_solution(self):
self.display_method = "Genetic algorithm"
self.display_parameters = {
'cr': self._cr_,
'mr': self._mr_,
'chromosomes': self._chromosomes_,
'selection_method': self._params_['selection_method'],
'crossover_method': self._params_['crossover_method'],
'mutation_method': self._params_['mutation_method']
}
return TSPSolver.get_solution(self)
def get_solution(self):
self.display_method = "Genetic"
self.display_parameters = {
'cr':
self._cr,
'mr':
self._mr,
'chromosomes':
self._chromosomes,
'initial_population_method':
self._g_args.get_method_initial_population(),
'selection_method':
self._g_args.get_method_select(),
'crossover_method':
self._g_args.get_method_cross(),
'mutation_method':
self._g_args.get_method_mutate()
}
if (self._g_args.get_method_initial_population() ==
INITIAL_POPULATION_SEMIACS):
self.display_parameters['initial_ants'] = \
self._g_args.get_initial_ants()
return TSPSolver.get_solution(self)
def get_solution(self):
self.display_method = "Brute force"
self.display_parameters = {'solution_canceled': self.solution_canceled}
return TSPSolver.get_solution(self)