def __init__(self, settings, function): # TODO add settings parameter
super(self.__class__, self).__init__()
# read in settings
num_dims = settings['number_of_dimensions']
population_size = settings['population_size']
bounds = settings['bounds']
if settings['velocity_type'] == 'constriction':
phi = max(settings['cp'] + settings['cg'], 4.0)
self.k = 2.0 / abs(2.0 - phi - sqrt(phi * phi - 4.0 * phi))
else:
self.k = 1
# check to make sure num_dims and number of bounds provided match
if len(bounds) != num_dims:
raise ValueError(
"Number of dimensions doesn't match number of bounds provided")
# set instance variables
self.settings = settings
self.function = function
# initialize population
self.population = PSO.__gen_population(bounds, population_size,
function)
self.total_population = population_size
self.best_x = PSO.__get_best_particle(self.population)
self.num_iterations = 1
if settings['plot']:
try:
self.plotutils = PlotUtils(num_dims, bounds, function)
self.__plot_state()
except ValueError:
print("Can not plot more than 2 dimensions")
settings['plot'] = False
if settings['print_iterations']:
self.__display_state()
if settings['step_through']:
oa_utils.pause()
def __init__(self, settings, function): # TODO add settings parameter
super(self.__class__, self).__init__()
# read in settings
num_dims = settings['number_of_dimensions']
population_size = settings['population_size']
bounds = settings['bounds']
# check to make sure num_dims and number of bounds provided match
if len(bounds) != num_dims:
raise ValueError("Number of dimensions doesn't match number of bounds provided")
# set instance variables
self.settings = settings
self.function = function
# initialize population
self.population = GA.__gen_population(bounds, population_size, function)
self.total_organisms = len(self.population)
self.best_x = self.population[0]
self.num_generations = 1
# stopping criteria variables
self.func_val_improvement = 0
self.num_iter_since_improvement = 0
if settings['plot']:
try:
self.plotutils = PlotUtils(num_dims, bounds, function)
self.__plot_state()
except ValueError:
print("Can not plot more than 2 dimensions")
settings['plot'] = False
if settings['print_iterations']:
self.__display_state()
if settings['step_through']:
oa_utils.pause()
def do_loop(self):
population = self.population
population = GA.__selection(population, \
self.settings['selection_cutoff'], \
self.settings['print_actions'])
population = GA.__crossover(self.total_organisms, \
population, \
self.settings['population_size'], \
self.function, \
self.settings['print_actions'])
self.total_organisms += len(population)
population = GA.__mutation(population, \
self.settings['bounds'], \
self.settings['mutation_rate'], \
self.settings['max_mutation_amount'], \
self.settings['print_actions'])
self.population = GA.__sort_population(population)
self.num_generations += 1
if self.population[0].fitness < self.best_x.fitness:
# add on the improvement in function value
self.func_val_improvement += (self.best_x.fitness - self.population[0].fitness)
self.best_x = self.population[0]
if self.settings['plot']:
self.__plot_state()
if self.settings['print_iterations']:
self.__display_state()
if self.settings['step_through']:
oa_utils.pause()
def do_loop(self):
population = self.population
population = PSO.__update_velocity(population, \
self.settings['velocity_type'], \
self.settings['print_actions'], \
self.get_best_x().pos, \
self.settings['cp'], \
self.settings['cg'], \
self.k, \
self.settings['weight'])
if self.settings['cg_plus']:
self.settings['cg'] += 0.1
phi = max(self.settings['cp'] + self.settings['cg'], 4.0)
self.k = 2.0 / abs(2.0 - phi - sqrt(phi * phi - 4.0 * phi))
population = PSO.__update_position(population)
self.num_iterations += 1
self.population = population
current_best = PSO.__get_best_particle(self.population)
if current_best.get_fval() < self.best_x.get_fval():
self.best_x = current_best
if self.settings['plot']:
self.__plot_state()
if self.settings['print_iterations']:
self.__display_state()
if self.settings['step_through']:
oa_utils.pause()
print("")
# time initialization
if settings['time']:
start_time = time.time()
# create algorithm instance
ga = GA(settings, function)
if settings['time']:
print(" --- Initialized in %s seconds --- " % (time.time() - start_time))
if settings['time_delay'] > 0.0 or settings['plot'] \
or settings['print_actions'] or settings['print_iterations'] or settings['step_through']:
print("\n --- WARNING: You are timing with either time_delay, plot, print_actions,")
print(" print_iterations, or step_through enabled. --- \n")
oa_utils.pause()
ga.start_timer()
#start_time = time.time()
ga.run()
if settings['time']:
ga.stop_timer()
print(" --- Ran for %s seconds --- " % (ga.get_time()))
#print(" --- Ran for %s seconds --- " % (time.time() - start_time))
# print out some data
print("")
print(str(ga))
sys.exit()