Ejemplos de HarmonySearch en Python

Ejemplo n.º 1

Archivo: test_harmony_search.py Proyecto: yangzilong1986/EPSoptimization

    def test_find_glocal_minimum_for_rosenbrook_function(self):

        rosenbrook_function_wrapper = RosenbrookFunctionWrapper()

        number_of_variables = 2
        objective = "minimization"

        harmony_search = HarmonySearch(rosenbrook_function_wrapper,
                                       number_of_variables, objective)

        maximum_attempt = 25000
        pitch_adjusting_range = 100
        harmony_search_size = 20
        harmony_memory_acceping_rate = 0.95
        pitch_adjusting_rate = 0.7

        result = harmony_search.search(
            maximum_attempt=maximum_attempt,
            pitch_adjusting_range=pitch_adjusting_range,
            harmony_search_size=harmony_search_size,
            harmony_memory_acceping_rate=harmony_memory_acceping_rate,
            pitch_adjusting_rate=pitch_adjusting_rate)

        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)

Ejemplo n.º 2

Archivo: command_line.py Proyecto: tadatoshi/metaheuristic_algorithms_python

def execute_harmony_search(args):
    objective_function = instantiate_objective_function(args.objective_function)

    from metaheuristic_algorithms.harmony_search import HarmonySearch
    harmony_search = HarmonySearch(objective_function, args.number_of_variables, args.objective)

    result = harmony_search.search(maximum_attempt = args.maximum_attempt, pitch_adjusting_range = args.pitch_adjusting_range, 
                                   harmony_search_size = args.harmony_search_size, harmony_memory_acceping_rate = args.harmony_memory_acceping_rate, 
                                   pitch_adjusting_rate = args.pitch_adjusting_rate)

    print('best_decision_variable_values=', result["best_decision_variable_values"])
    print('best_objective_function_value=', result["best_objective_function_value"])

Ejemplo n.º 3

def execute_harmony_search(args):
    objective_function = instantiate_objective_function(
        args.objective_function)

    from metaheuristic_algorithms.harmony_search import HarmonySearch
    harmony_search = HarmonySearch(objective_function,
                                   args.number_of_variables, args.objective)

    result = harmony_search.search(
        maximum_attempt=args.maximum_attempt,
        pitch_adjusting_range=args.pitch_adjusting_range,
        harmony_search_size=args.harmony_search_size,
        harmony_memory_acceping_rate=args.harmony_memory_acceping_rate,
        pitch_adjusting_rate=args.pitch_adjusting_rate)

    print('best_decision_variable_values=',
          result["best_decision_variable_values"])
    print('best_objective_function_value=',
          result["best_objective_function_value"])

Ejemplo n.º 4

Archivo: test_harmony_search.py Proyecto: tadatoshi/metaheuristic_algorithms_python

    def test_find_glocal_minimum_for_rosenbrook_function(self):

        rosenbrook_function_wrapper = RosenbrookFunctionWrapper()

        number_of_variables = 2
        objective = "minimization"

        harmony_search = HarmonySearch(rosenbrook_function_wrapper, number_of_variables, objective)

        maximum_attempt = 25000
        pitch_adjusting_range = 100
        harmony_search_size = 20
        harmony_memory_acceping_rate = 0.95
        pitch_adjusting_rate = 0.7

        result = harmony_search.search(maximum_attempt = maximum_attempt, pitch_adjusting_range = pitch_adjusting_range, 
                                       harmony_search_size = harmony_search_size, harmony_memory_acceping_rate = harmony_memory_acceping_rate, 
                                       pitch_adjusting_rate = pitch_adjusting_rate)

        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)