def __init__(self):
self.iteration_count = 0
self.jump_function = p2.JumpFunction()
self.best_jump_function = copy.deepcopy(self.jump_function)
# self.jump_function.print_board_and_objective_function()
self.get_interation_count()
self.perform_stochastic_local_search()
self.print_best_objective_value()
def __init__(self):
self.iteration_count = 0
self.probability = 0
self.jump_function = p2.JumpFunction()
self.best_jump_function = copy.deepcopy(self.jump_function)
self.get_interation_count()
self.get_hill_descent_count()
self.perform_hill_descent_with_random_uphill_steps()
self.print_best_objective_value()
def __init__(self):
self.iteration_count = 0
self.hill_descent_count = 0
self.jump_function = p2.JumpFunction()
self.best_jump_function = copy.deepcopy(self.jump_function)
self.get_interation_count()
self.get_hill_descent_count()
self.perform_random_restart_hill_descent()
self.print_best_objective_value()
def __init__(self):
self.iteration_count = 0
self.temperature = 0
self.decay_rate = 0
self.jump_function = p2.JumpFunction()
self.best_jump_function = copy.deepcopy(self.jump_function)
self.get_interation_count()
self.get_initial_temperature()
self.get_decay_rate()
self.perform_simulated_annealing()
self.print_best_objective_value()
def perform_random_restart_hill_descent(self):
for i in range(self.hill_descent_count):
for j in range(self.iteration_count):
jump_function_prime = copy.deepcopy(self.jump_function)
self.change_random_jump_number(jump_function_prime.rjm_board,
jump_function_prime.board_size)
jump_function_prime.rerun_objective_function()
if jump_function_prime.objective_value <= self.jump_function.objective_value:
self.jump_function = copy.deepcopy(jump_function_prime)
if jump_function_prime.objective_value <= self.best_jump_function.objective_value:
self.best_jump_function = copy.deepcopy(
self.jump_function)
self.jump_function = p2.JumpFunction()