def get_random_seed(self):
return self._random_seed
def get_max_imp(self):
return self._max_imp
def get_hmcr(self):
return self._hmcr
def get_par(self):
return self._par
def get_hms(self):
return self._hms
def get_mpai(self):
return self._mpai
def get_mpap(self):
return self._mpap
def maximize(self):
return self._maximize
if __name__ == '__main__':
obj_fun = ObjectiveFunction()
num_processes = 1
num_iterations = 1 # because random_seed is defined, there's no point in running this multiple times
results = harmony_search(obj_fun, num_processes, num_iterations)
print('Elapsed time: {}\nBest harmony: {}\nBest fitness: {}'.format(results.elapsed_time, results.best_harmony, results.best_fitness))
def get_max_imp(self):
return self._max_imp
def get_hmcr(self):
return self._hmcr
def get_par(self):
return self._par
def get_hms(self):
return self._hms
def get_mpai(self):
return self._mpai
def get_mpap(self):
return self._mpap
def maximize(self):
return self._maximize
if __name__ == '__main__':
obj_fun = ObjectiveFunction()
num_processes = cpu_count() # use number of logical CPUs
num_iterations = num_processes * 5 # each process does 5 iterations
results = harmony_search(obj_fun, num_processes, num_iterations)
print('Elapsed time: {}\nBest harmony: {}\nBest fitness: {}'.format(
results.elapsed_time, results.best_harmony, results.best_fitness))
def get_par(self):
#TODO implement pitch adjustment rate accroding to http://scialert.net/qredirect.php?doi=jas.2013.633.638&linkid=pdf
return self._par
def get_hms(self):
return self._hms
def get_mpai(self):
return self._mpai
def get_mpap(self):
#TODO remove, when it runs
return 0.5
def maximize(self):
return self._maximize
from problemParser import parse_problem
from docopt import docopt
if __name__ == '__main__':
arguments = docopt(__doc__)
problem_instance = parse_problem(arguments['<problem_instance>'])
obj_fun = VRPTWObjectiveFunction(arguments, problem_instance)
num_processes = cpu_count() - 1 #use number of logical CPUs - 1 so that I have one available for use
num_processes = 1
num_iterations = 100
(result, value) = (harmony_search(obj_fun, num_processes, num_iterations))
print obj_fun.make_x_from_vector(result)
print value
return self._random_seed
def get_max_imp(self):
return self._max_imp
def get_hmcr(self):
return self._hmcr
def get_par(self):
return self._par
def get_hms(self):
return self._hms
def get_mpai(self):
return self._mpai
def get_mpap(self):
return self._mpap
def maximize(self):
return self._maximize
if __name__ == '__main__':
obj_fun = ObjectiveFunction()
num_processes = 1
num_iterations = 1 # because random_seed is defined, there's no point in running this multiple times
initial_harmonies = [[random.randint(-1000, 1000) for _ in range(2)] for _ in range(100)]
results = harmony_search(obj_fun, num_processes, num_iterations, initial_harmonies=initial_harmonies)
print('Elapsed time: {}\nBest harmony: {}\nBest fitness: {}'.format(results.elapsed_time, results.best_harmony, results.best_fitness))
def use_random_seed(self): return hasattr(self, '_random_seed') and self._random_seed def get_max_imp(self): return self._max_imp def get_hmcr(self): return self._hmcr def get_par(self): return self._par def get_hms(self): return self._hms def get_mpai(self): return self._mpai def get_mpap(self): return self._mpap def maximize(self): return self._maximize if __name__ == '__main__': obj_fun = ObjectiveFunction() num_processes = cpu_count() #use number of logical CPUs num_iterations = num_processes * 5 #each process does 5 iterations print(harmony_search(obj_fun, num_processes, num_iterations))