def calculate(times):
population = Species_origin(Constant.population_size)
next_species_result = population
max_result_list = []
max_powerselection_list = []
for i in range(times):
translation_result = translation(
next_species_result) # translation result 也是二维数组
output_max_result = test_output_max(translation_result)
max_result = output_max_result[0]
maxresult_number = output_max_result[1]
max_result_list.append(max_result)
max_temporary = translation_result[maxresult_number]
max_powerselection_list.append(max_temporary)
print("time:", i, "maxresult:", max_result)
newpopulation_result = newpopulation(next_species_result)
fitness_result = fitness(newpopulation_result)
selection_result = selection(fitness_result)
next_species_result = nextspecies(newpopulation_result,
selection_result)
print("max_result_list:", max_result_list)
perfect = max(max_result_list)
print("the most max:", perfect)
j = 0
while max_result_list[j] != perfect:
j = j + 1
print("the most max temporary selection:", max_powerselection_list[j])
def calculate(times, mover, number, mode):
'''
if mover == 0: # 燃气轮机
population = Species_origin(Constant.population_size)
else: # 内燃机
population = SpeciesOriginICE(Constant.population_size).population
'''
population = [[21, 5, 15, 35, 63, 0, 0, 0], [16, 9, 16, 69, 40, 0, 0, 0],
[30, 18, 17, 77, 81, 0, 0, 0], [29, 26, 18, 32, 87, 0, 0, 0],
[15, 11, 13, 80, 25, 0, 0, 0], [16, 2, 15, 80, 44, 0, 0, 0],
[28, 22, 19, 56, 56, 0, 0, 0], [16, 5, 16, 60, 31, 0, 0, 0],
[22, 4, 18, 79, 55, 0, 0, 0], [29, 15, 25, 45, 78, 0, 0, 0]]
next_species_result = population
print("the original", next_species_result)
max_result_list = []
max_powerselection_list = []
for i in range(times):
translation_result = translation(
next_species_result) # translation result 也是二维数组
if mover == 0:
output_max_result = output_max(translation_result, mode)
max_result = output_max_result[0]
maxresult_number = output_max_result[1]
else:
output_max_result = OutputMaxICE(translation_result, number, mode)
max_result = output_max_result.max_profit_result
maxresult_number = output_max_result.array_number
max_result_list.append(max_result)
max_temporary = translation_result[maxresult_number]
max_powerselection_list.append(max_temporary)
print("time:", i, "maxresult:", max_result)
print("")
newpopulation_result = newpopulation(next_species_result, mover)
if mover == 0:
fitness_result = fitness(newpopulation_result, mode)
else:
fitness_result = FitnessICE(newpopulation_result, number,
mode).fitness1
selection_result = selection(fitness_result)
next_species_result = nextspecies(newpopulation_result,
selection_result)
print("max_result_list:", max_result_list)
perfect = max(max_result_list)
print("the most max:", perfect)
perfect_number = 0
while max_result_list[perfect_number] != perfect:
perfect_number = perfect_number + 1
print("the most max temporary selection:",
max_powerselection_list[perfect_number])
economy_index = EconomyIndex(max_powerselection_list[perfect_number],
mover, number, mode)
print("IRR:", economy_index.IRR)
print("PaybackPeriod:", economy_index.PaybackPeriod)
print("ReturnOnCapital:", economy_index.ReturnOnCapital)
# 将数组 min_result_list (遗传算法每次最优结果)的值写入excel中
return 0
def calculate(times, mover, number, season, mode):
if mover == 0: # 燃气轮机
population = Species_origin(Constant.population_size)
else: # 内燃机
population = SpeciesOriginICE(Constant.population_size).population
next_species_result = population
print("the original", next_species_result)
max_result_list = []
max_powerselection_list = []
for i in range(times):
translation_result = translation(
next_species_result) # translation result 也是二维数组
if mover == 0:
output_max_result = output_max(translation_result, mode)
max_result = output_max_result[0]
maxresult_number = output_max_result[1]
else:
output_max_result = OutputMaxICE(translation_result, number,
season, mode)
max_result = output_max_result.max_profit_result
maxresult_number = output_max_result.array_number
max_result_list.append(max_result)
max_temporary = translation_result[maxresult_number]
max_powerselection_list.append(max_temporary)
print("time:", i, "maxresult:", max_result)
print("")
newpopulation_result = newpopulation(next_species_result, mover)
if mover == 0:
fitness_result = fitness(newpopulation_result, mode)
else:
fitness_result = FitnessICE(newpopulation_result, number, season,
mode).fitness1
selection_result = selection(fitness_result)
next_species_result = nextspecies(newpopulation_result,
selection_result)
print("max_result_list:", max_result_list)
perfect = max(max_result_list)
print("the most max:", perfect)
perfect_number = 0
while max_result_list[perfect_number] != perfect:
perfect_number = perfect_number + 1
print("the most max temporary selection:",
max_powerselection_list[perfect_number])
economy_index = EconomyIndex(max_powerselection_list[perfect_number],
mover, number, season, mode)
print("IRR:", economy_index.IRR)
print("PaybackPeriod:", economy_index.PaybackPeriod)
print("ReturnOnCapital:", economy_index.ReturnOnCapital)
# 将数组 min_result_list (遗传算法每次最优结果)的值写入excel中
return 0
def __init__(self, times, mover, number, season, mode, way_pareto): # way_pareto=0:正pareto，其他：反pareto
if mover == 1:
# population = SpeciesOriginICE(Constant.population_size).population
population = [[21, 5, 15, 35, 63, 92, 37, 65], [16, 9, 16, 69, 40, 34, 20, 91],
[30, 18, 17, 77, 81, 52, 98, 99], [29, 26, 18, 32, 87, 77, 86, 32],
[15, 11, 13, 80, 25, 77, 62, 97], [16, 2, 15, 80, 44, 18, 76, 29],
[28, 22, 19, 56, 56, 37, 36, 32], [16, 5, 16, 60, 31, 71, 57, 55],
[22, 4, 18, 79, 55, 81, 84, 28], [29, 15, 25, 45, 78, 94, 96, 71]]
next_species_result = population
print("the original", next_species_result)
self.pareto_best = []
if way_pareto == 0:
pareto_sorting = ParetoSorting(next_species_result, number, season, mode)
else:
pareto_sorting = ReverseParetoSorting(next_species_result, number, season, mode)
print("original_best:", pareto_sorting.pareto_sorting_result)
for i in range(times):
solution_best_of_this_generation = []
new_population = newpopulation(next_species_result, mover) # 遗传变异
print("new_population:", new_population)
selecting_process = NextGeneration(new_population, number, season, mode, way_pareto)
selecting_queue = selecting_process.selecting_queue
print("selecting_queue:", selecting_queue)
next_species_result = []
for m in range(Constant.population_size):
next_species_result.append(new_population[selecting_queue[m]])
print("next_species_result:", next_species_result)
for n in range(selecting_process.length_of_best):
solution_best_of_this_generation.append(next_species_result[n])
self.pareto_best.append(solution_best_of_this_generation)
print("time:", i, self.pareto_best[i])
# 列出每代最优解之集合的最优解，即看最优解的进化过程
set_of_best = []
self.evaluation_of_best = []
self.average_evaluation_of_cost = []
self.average_evaluation_of_emission = []
self.average_evaluation_of_pei = []
self.best_evaluation_of_cost = []
self.best_evaluation_of_emission = []
self.best_evaluation_of_pei = []
self.list_evaluation_of_cost = []
self.list_evaluation_of_emission = []
self.list_evaluation_of_pei = []
for i in range(times):
for j in range(len(self.pareto_best[i])):
set_of_best.append(self.pareto_best[i][j])
if way_pareto == 0:
pareto_again = ParetoSorting(set_of_best, number, season, mode)
else:
pareto_again = ReverseParetoSorting(set_of_best, number, season, mode)
self.evaluation_of_best.append(pareto_again.pareto_sorting_best)
self.list_evaluation_of_cost.append(pareto_again.pareto_sorting_best_cost_result)
self.list_evaluation_of_emission.append(pareto_again.pareto_sorting_best_emission_result)
self.list_evaluation_of_pei.append(pareto_again.pareto_sorting_best_pei_result)
length = len(self.list_evaluation_of_cost[i])
self.average_evaluation_of_cost.append(sum(self.list_evaluation_of_cost[i]) / length)
self.average_evaluation_of_emission.append(sum(self.list_evaluation_of_emission[i]) / length)
self.average_evaluation_of_pei.append(sum(self.list_evaluation_of_pei[i]) / length)
self.best_evaluation_of_cost.append(min(self.list_evaluation_of_cost[i]))
self.best_evaluation_of_emission.append(min(self.list_evaluation_of_emission[i]))
self.best_evaluation_of_pei.append(max(self.list_evaluation_of_pei[i]))
print("pareto again:", i)
def __init__(self, times, mover, number, mode, way_pareto, storage_or_not): # way_pareto=0:正pareto，其他：反pareto
if mover == 1:
# population = SpeciesOriginICE(Constant.population_size, storage_or_not).population
if storage_or_not == 1: # 有储能
population = [[21, 17, 16, 35, 63, 20, 22, 24], [30, 27, 37, 69, 40, 34, 44, 31],
[40, 38, 30, 76, 81, 50, 54, 60], [50, 45, 43, 32, 87, 27, 86, 32],
[60, 50, 52, 80, 25, 77, 62, 50], [70, 67, 57, 60, 44, 18, 72, 39],
[80, 72, 76, 26, 56, 20, 36, 52], [90, 83, 70, 60, 31, 71, 57, 55],
[55, 30, 40, 79, 66, 43, 36, 28], [45, 39, 40, 45, 78, 74, 34, 45]]
else: # 无储能
population = [[21, 17, 16, 35, 63, 0, 0, 0], [30, 27, 37, 69, 40, 0, 0, 0],
[40, 38, 30, 76, 81, 0, 0, 0], [50, 45, 43, 32, 87, 0, 0, 0],
[60, 50, 52, 80, 25, 0, 0, 0], [70, 67, 57, 60, 44, 0, 0, 0],
[80, 72, 76, 26, 56, 0, 0, 0], [90, 83, 70, 60, 31, 0, 0, 0],
[55, 30, 40, 79, 66, 0, 0, 0], [45, 39, 40, 45, 78, 0, 0, 0]]
next_species_result = population
print("the original", next_species_result)
self.pareto_best = []
if way_pareto == 0:
pareto_sorting = ParetoSorting(next_species_result, number, mode)
else:
pareto_sorting = ReverseParetoSorting(next_species_result, number, mode)
print("original_best:", pareto_sorting.pareto_sorting_result)
for i in range(times):
solution_best_of_this_generation = []
new_population = newpopulation(next_species_result, mover) # 遗传变异
# print("new_population:", new_population)
selecting_process = NextGeneration(new_population, number, mode, way_pareto)
selecting_queue = selecting_process.selecting_queue
# print("selecting_queue:", selecting_queue)
next_species_result = []
for m in range(Constant.population_size):
next_species_result.append(new_population[selecting_queue[m]])
# print("next_species_result:", next_species_result)
for n in range(selecting_process.length_of_best):
solution_best_of_this_generation.append(next_species_result[n])
self.pareto_best.append(solution_best_of_this_generation)
print("time:", i, self.pareto_best[i]) # 每代最优解集合
# 列出每代最优解之集合的最优解，即看最优解的进化过程
set_of_best = []
self.evaluation_of_best = []
self.average_evaluation_of_cost = []
self.average_evaluation_of_emission = []
self.average_evaluation_of_pei = []
self.best_evaluation_of_cost = []
self.best_evaluation_of_emission = []
self.best_evaluation_of_pei = []
self.list_evaluation_of_cost = []
self.list_evaluation_of_emission = []
self.list_evaluation_of_pei = []
self.list_evaluation_of_ele_waste_0 = []
self.list_evaluation_of_ele_waste_1 = []
self.list_evaluation_of_ele_waste_2 = []
self.list_evaluation_of_heat_waste = []
self.list_evaluation_of_cold_waste = []
self.average_evaluation_of_ele_waste_0 = []
self.average_evaluation_of_ele_waste_1 = []
self.average_evaluation_of_ele_waste_2 = []
self.average_evaluation_of_heat_waste = []
self.average_evaluation_of_cold_waste = []
for i in range(times):
for j in range(len(self.pareto_best[i])):
set_of_best.append(self.pareto_best[i][j])
remove_the_same = RemoveTheSame(set_of_best) # 去除重复项
set_of_best_after = remove_the_same.after_removing
if way_pareto == 0:
pareto_again = ParetoSorting(set_of_best_after, number, mode)
else:
pareto_again = ReverseParetoSorting(set_of_best_after, number, mode)
self.evaluation_of_best.append(pareto_again.pareto_sorting_best) # pareto最高层级的
self.list_evaluation_of_cost.append(pareto_again.pareto_sorting_best_cost_result)
self.list_evaluation_of_emission.append(pareto_again.pareto_sorting_best_emission_result)
self.list_evaluation_of_pei.append(pareto_again.pareto_sorting_best_pei_result)
self.list_evaluation_of_ele_waste_0.append(pareto_again.ele_waste_0)
self.list_evaluation_of_ele_waste_1.append(pareto_again.ele_waste_1)
self.list_evaluation_of_ele_waste_2.append(pareto_again.ele_waste_2)
self.list_evaluation_of_heat_waste.append(pareto_again.heat_waste)
self.list_evaluation_of_cold_waste.append(pareto_again.cold_waste)
length = len(self.list_evaluation_of_cost[i])
self.average_evaluation_of_cost.append(sum(self.list_evaluation_of_cost[i]) / length)
self.average_evaluation_of_emission.append(sum(self.list_evaluation_of_emission[i]) / length)
self.average_evaluation_of_pei.append(sum(self.list_evaluation_of_pei[i]) / length)
self.average_evaluation_of_ele_waste_0.append(sum(self.list_evaluation_of_ele_waste_0[i]) / length)
self.average_evaluation_of_ele_waste_1.append(sum(self.list_evaluation_of_ele_waste_1[i]) / length)
self.average_evaluation_of_ele_waste_2.append(sum(self.list_evaluation_of_ele_waste_2[i]) / length)
self.average_evaluation_of_heat_waste.append(sum(self.list_evaluation_of_heat_waste[i]) / length)
self.average_evaluation_of_cold_waste.append(sum(self.list_evaluation_of_cold_waste[i]) / length)
self.best_evaluation_of_cost.append(min(self.list_evaluation_of_cost[i]))
self.best_evaluation_of_emission.append(min(self.list_evaluation_of_emission[i]))
self.best_evaluation_of_pei.append(max(self.list_evaluation_of_pei[i]))
print("pareto again:", i)