def execute_simulated_annealing(args):
objective_function = instantiate_objective_function(
args.objective_function)
from metaheuristic_algorithms.simulated_annealing import SimulatedAnnealing
simulated_annealing = SimulatedAnnealing(objective_function,
args.number_of_variables,
args.objective)
result = simulated_annealing.search(
args.temperature,
args.minimal_temperature,
args.bolzmann_constant,
args.energy_norm,
maximum_number_of_rejections=args.maximum_number_of_rejections,
maximum_number_of_runs=args.maximum_number_of_runs,
maximum_number_of_acceptances=args.maximum_number_of_acceptances,
cooling_factor=args.cooling_factor,
standard_diviation_for_estimation=args.
standard_diviation_for_estimation,
ratio_of_energy_delta_over_evaluation_delta=args.
ratio_of_energy_delta_over_evaluation_delta)
print('best_decision_variable_values=',
result["best_decision_variable_values"])
print('best_objective_function_value=',
result["best_objective_function_value"])
def test_find_glocal_minimum_for_rosenbrook_function(self):
rosenbrook_function_wrapper = RosenbrookFunctionWrapper()
number_of_variables = 2
objective = "minimization"
simulated_annealing = SimulatedAnnealing(rosenbrook_function_wrapper,
number_of_variables,
objective)
temperature = 1.0
minimal_temperature = 1e-10 # Final stopping temperature
# minimal_temperature = 1e-1 # Final stopping temperature
maximum_number_of_rejections = 2500
# maximum_number_of_rejections = 1000
maximum_number_of_runs = 500
# maximum_number_of_runs = 100
maximum_number_of_acceptances = 15
bolzmann_constant = 1
cooling_factor = 0.95
energy_norm = 1e-8
# energy_norm = 1e-1
standard_diviation_for_estimation = 1
ratio_of_energy_delta_over_evaluation_delta = 1
result = simulated_annealing.search(
temperature,
minimal_temperature,
bolzmann_constant,
energy_norm,
maximum_number_of_rejections=maximum_number_of_rejections,
maximum_number_of_runs=maximum_number_of_runs,
maximum_number_of_acceptances=maximum_number_of_acceptances,
cooling_factor=cooling_factor,
standard_diviation_for_estimation=standard_diviation_for_estimation,
ratio_of_energy_delta_over_evaluation_delta=
ratio_of_energy_delta_over_evaluation_delta)
self.assertAlmostEqual(result["best_decision_variable_values"][0],
1.0112,
delta=1)
self.assertAlmostEqual(result["best_decision_variable_values"][1],
0.9988,
delta=1)
self.assertAlmostEqual(result["best_objective_function_value"],
0.0563,
delta=1)
def execute_simulated_annealing(args):
objective_function = instantiate_objective_function(args.objective_function)
from metaheuristic_algorithms.simulated_annealing import SimulatedAnnealing
simulated_annealing = SimulatedAnnealing(objective_function, args.number_of_variables, args.objective)
result = simulated_annealing.search(args.temperature, args.minimal_temperature,
args.bolzmann_constant, args.energy_norm,
maximum_number_of_rejections = args.maximum_number_of_rejections,
maximum_number_of_runs = args.maximum_number_of_runs,
maximum_number_of_acceptances = args.maximum_number_of_acceptances,
cooling_factor = args.cooling_factor,
standard_diviation_for_estimation = args.standard_diviation_for_estimation,
ratio_of_energy_delta_over_evaluation_delta = args.ratio_of_energy_delta_over_evaluation_delta)
print('best_decision_variable_values=', result["best_decision_variable_values"])
print('best_objective_function_value=', result["best_objective_function_value"])
def test_find_glocal_minimum_for_rosenbrook_function(self):
rosenbrook_function_wrapper = RosenbrookFunctionWrapper()
number_of_variables = 2
objective = "minimization"
simulated_annealing = SimulatedAnnealing(rosenbrook_function_wrapper, number_of_variables, objective)
temperature = 1.0
minimal_temperature = 1e-10 # Final stopping temperature
# minimal_temperature = 1e-1 # Final stopping temperature
maximum_number_of_rejections = 2500
# maximum_number_of_rejections = 1000
maximum_number_of_runs = 500
# maximum_number_of_runs = 100
maximum_number_of_acceptances = 15
bolzmann_constant = 1
cooling_factor = 0.95
energy_norm = 1e-8
# energy_norm = 1e-1
standard_diviation_for_estimation = 1
ratio_of_energy_delta_over_evaluation_delta = 1
result = simulated_annealing.search(temperature, minimal_temperature,
bolzmann_constant, energy_norm,
maximum_number_of_rejections = maximum_number_of_rejections,
maximum_number_of_runs = maximum_number_of_runs,
maximum_number_of_acceptances = maximum_number_of_acceptances,
cooling_factor = cooling_factor,
standard_diviation_for_estimation = standard_diviation_for_estimation,
ratio_of_energy_delta_over_evaluation_delta = ratio_of_energy_delta_over_evaluation_delta)
self.assertAlmostEqual(result["best_decision_variable_values"][0], 1.0112, delta = 1)
self.assertAlmostEqual(result["best_decision_variable_values"][1], 0.9988, delta = 1)
self.assertAlmostEqual(result["best_objective_function_value"], 0.0563, delta = 1)