def hill_climbing(lista_drones, global_max):
""" Hill climbing algorithm. It receives the list of drones and the global maximum"""
#global partial
#global_variables.partial = 1 # we evaluate hill climbing for the initial deployment problem
global_variables.partial = 0
records = []
records.append(global_max)
first_max = global_max
max_list = copy.deepcopy(lista_drones) # watch up
max_speed = 10 # meters/seconds
simulation_time = 50000 # seconds
#choose_directions(lista_drones, max_speed) # here we selec the type of movement
selected = choose_directions_single(lista_drones, max_speed)
current_quality = 0.0
prueba = lista_drones
best_time = 0
print("---------- START CLIMBING OPTIMIZATION ----------")
for i in range(0, simulation_time):
update_drones_positions(lista_drones, 0)
#update_drones_positions_single(lista_drones, selected)
current_quality = quality.evaluate(lista_drones)
if (current_quality <= global_max):
#selected= step_back_single(lista_drones, selected, max_speed)
step_back(lista_drones, max_speed)
#print("go back")
else:
print("----------------previous quality %f" % global_max)
global_max = current_quality
#records.append(global_max)
prueba = copy.deepcopy(lista_drones)
print("----------------new quality %f" % global_max)
plots.print_drones_data(lista_drones, 0)
best_time = i # we update the time at which we find the best solution so far
print("-----------------------------------------------")
print("-----------------------------------------------")
# we print some information about how the positions are updated.
print("quality before hill climbing %f" % first_max)
print("List of victims covered %s" %
quality.calc_victims_covered(max_list))
print("total number of victims covered %d" %
len(quality.calc_victims_covered(max_list)))
print("-------INITIAL POSITIONS----------------------")
plots.print_drones_data(max_list, 0)
print("----------------------------------------------")
print("----------------------------------------------")
print("final quality hill climbing %f" % global_max)
print("List of victims covered %s" % quality.calc_victims_covered(prueba))
print("total number of victims covered %d" %
len(quality.calc_victims_covered(prueba)))
print("-------FINAL POSITIONS----------------------")
plots.print_drones_data(prueba, 0)
print("----------------------------------------------")
print("----------------------------------------------")
return prueba, records
def simulated_annealing(lista_drones, global_max, deviation):
""" Simulated annealing algorithm. It receives the list of drones and the global maximum"""
max_speed = 10 # meters/seconds
simulation_time = 10000 # seconds
global_variables.partial = 0
temperature = float(deviation) / 0.0953
records = []
records_positions = []
records_probability = []
records_temperature = []
records_aux = []
global_max2, = global_max
first_max = global_max
max_list = copy.deepcopy(lista_drones) # watch up
records.append(global_max2)
records_positions.append(max_list)
choose_directions(lista_drones, max_speed)
print("---------- START SIMULATED ANNEALING OPTIMIZATION ----------")
for i in range(0, simulation_time):
update_drones_positions(lista_drones)
current_quality, = quality.evaluate(lista_drones)
if current_quality <= global_max2 and current_quality > 0:
probability = calculate_annealing_probability(
temperature, current_quality, global_max2)
print(probability)
records_probability.append(probability)
#records_aux.append(aux)
if (probability >=
random.random()): # we should avoid non valid solutions
print("----------------accepting a WORSE solution--------")
print("----------------previous quality %f" % global_max2)
global_max2 = current_quality
records.append(global_max2)
prueba = copy.deepcopy(lista_drones)
records_positions.append(prueba)
print("----------------new quality %f" % global_max2)
plots.print_drones_data(lista_drones, 0)
print("-----------------------------------------------")
print("-----------------------------------------------")
else:
step_back(lista_drones, max_speed)
else:
if (current_quality > 0):
print("----------------accepting a BETTER solution--------")
print("----------------previous quality %f" % global_max2)
global_max2 = current_quality
prueba = copy.deepcopy(lista_drones)
records.append(global_max2)
records_positions.append(prueba)
print("----------------new quality %f" % global_max2)
plots.print_drones_data(prueba, 0)
print("-----------------------------------------------")
print("-----------------------------------------------")
temperature = update_annealing_temperature(temperature, deviation,
simulation_time)
records_temperature.append(temperature)
print("temperature %f" % temperature)
#plots.evolution_local(records_aux, "probability")
index = miscelleneous.find_max(records)
print("quality before simulated annealing %f" % first_max)
print("List of victims covered %s" %
quality.calc_victims_covered(max_list))
print("total number of victims covered %d" %
len(quality.calc_victims_covered(max_list)))
print("-------INITIAL POSITIONS----------------------")
plots.print_drones_data(max_list, 0)
print("----------------------------------------------")
print("----------------------------------------------")
print("final quality simulated annealing %f" % records[index])
print("List of victims covered %s" %
quality.calc_victims_covered(records_positions[index]))
print("total number of victims covered %d" %
len(quality.calc_victims_covered(records_positions[index])))
print("-------FINAL POSITIONS----------------------")
plots.print_drones_data(records_positions[index], 0)
print("----------------------------------------------")
print("----------------------------------------------")
return records_positions[index], records
def tabu_search(lista_drones, global_max):
""" Tabu search algorithm. It receives the list of drones and the global maximum"""
#global partial
global_variables.partial = 1
records = []
records.append(global_max)
movement_list = []
tabu_list = [] # store the positions that have already visited
first_max = global_max # we store the first maximum gloabal
max_list = copy.deepcopy(
lista_drones) # make a copy of the original postions of the drones
max_speed = 10 # meters/seconds
simulation_time = 10000 # seconds
choose_directions(lista_drones, max_speed)
current_quality = 0.0
repetido = 0
print("---------- START TABU SEARCH OPTIMIZATION ----------")
for i in range(0, simulation_time):
update_drones_positions(lista_drones, 0)
current_quality = quality.evaluate(lista_drones)
current_positions = miscelleneous.get_positions(lista_drones)
if current_positions in tabu_list:
repetido = repetido + 1
print("REPETIDOOOOOOOOOOOOOOOOOOOOOOOO")
else:
if (current_quality <= global_max):
step_back(lista_drones, max_speed)
#print("go back")
else:
print(current_positions)
update_movements(lista_drones)
movement_list.append(current_positions)
print("----------------previous quality %f" % global_max)
global_max = current_quality
records.append(global_max)
prueba = copy.deepcopy(lista_drones)
print("----------------new quality %f" % global_max)
plots.print_drones_data(lista_drones, 0)
print("-----------------------------------------------")
print("-----------------------------------------------")
tabu_list.append(current_positions)
print("repetido %d" % repetido)
print("quality before tabu search %f" % first_max)
print("List of victims covered %s" %
quality.calc_victims_covered(max_list))
print("total number of victims covered %d" %
len(quality.calc_victims_covered(max_list)))
print("-------INITIAL POSITIONS----------------------")
plots.print_drones_data(max_list, 0)
print("----------------------------------------------")
print("----------------------------------------------")
print("final quality tabu search %f" % global_max)
print("List of victims covered %s" % quality.calc_victims_covered(prueba))
print("total number of victims covered %d" %
len(quality.calc_victims_covered(prueba)))
print("-------FINAL POSITIONS----------------------")
plots.print_drones_data(prueba, 0)
print("----------------------------------------------")
print("----------------------------------------------")
plots.plot_movements(lista_drones)
return prueba, records