def __init__(self):
# パラメータ
n_dim = 10
pop_size = 200
epoch = 200
imax = 100 # 対象デブリ数
# 問題
problem = Problem(n_dim)
initializer = Initializer(n_dim, imax)
# selection = TournamentSelection(ksize=2)
selection = TournamentSelectionStrict(ksize=2)
# selection = TournamentSelectionDCD()
# crossover = BlendCrossover(alpha=0.5)
crossover = Crossover(ix=OrderCrossover(rate=0.9),
dx=SimulatedBinaryCrossover(rate=0.9, eta=20))
mutation = Mutation(im=SwapMutation(rate=0.1),
dm=PolynomialMutation(rate=0.1, eta=20))
optimizer = NSGA2(pop_size, selection, crossover, mutation)
optimizer.setup(problem)
### Additional setting ###
Genome.set_weight([10, 3600] * n_dim)
Individual.set_weight([1, -1])
optimizer.initializer = initializer
optimizer.n_cycle = pop_size // 2
# optimizer.alternation = 'replace'
##########################
self.optimizer = optimizer
def __init__(self, popsize=100, ksize=5):
# パラメータ
n_dim = 10
# popsize = 200
# epoch = 100
imax = 100 # 対象デブリ数
# ksize = 5
# 問題
problem = Problem(n_dim)
with Environment() as env:
# 個体クラス
indiv_type = Individual
# 初期個体生成クラス
indiv_pool = env.register(indiv_type)
# 遺伝子生成クラス
initializer = Initializer(n_dim, imax)
# GAオペレータ指定
# selection = TournamentSelection(ksize=2)
selection = TournamentSelectionStrict(ksize=2)
# selection = TournamentSelectionDCD()
# crossover = BlendCrossover(alpha=0.5)
crossover = Crossover(ix=OrderCrossover(rate=0.9),
dx=SimulatedBinaryCrossover(rate=0.9,
eta=20))
mutation = Mutation(im=SwapMutation(rate=0.1),
dm=PolynomialMutation(rate=0.1, eta=20))
ga_ops = {
'selection': selection,
'crossover': crossover,
'mutation': mutation
}
# optimizer = NSGA2(problem=problem, pool=indiv_pool, **ga_ops)
optimizer = MOEAD(problem=problem,
pool=indiv_pool,
ksize=5,
**ga_ops)
### Additional setting ###
Genome.set_weight([1, 3600] * n_dim)
Individual.set_weight([1, -1])
# optimizer.initializer = initializer
optimizer.n_cycle = popsize // 2
optimizer.alternation = 'all'
##########################
# 個体生成器
creator = Creator(initializer, indiv_pool)
# 登録
env.optimizer = optimizer
env.creator = creator
self.env = env
def main(model, out):
n_dim = 10
popsize = 30
epoch = 100 * 5
problem = Problem()
with Environment() as env:
indiv_pool = env.register(Individual)
indiv_pool.cls.set_weight([1, -1])
initializer = UniformInitializer(n_dim)
creator = Creator(initializer, indiv_pool)
crossover = SimulatedBinaryCrossover(rate=0.9, eta=100)
if model == 'moead':
ksize = 10
options = {
"ksize": ksize,
"normalization": True,
"crossover": crossover
}
optimizer = MOEAD(problem=problem, pool=indiv_pool, **options)
optimizer.weight_generator(nobj=4, divisions=50)
popsize = int(popsize)
epoch = epoch
elif model == 'nsga2':
optimizer = NSGA2(problem=problem,
pool=indiv_pool,
normalization=False)
elif model == 'para':
optimizer = NSGA2_para(problem=problem, pool=indiv_pool)
else:
raise Exception('Unexpected model name')
population = optimizer.init_population(creator, popsize=popsize)
history = [population]
print("obj weight:", population[0].data.weight)
for i in range(1, epoch + 1):
if i % 50 == 0:
print("epoch ", i)
population = optimizer(population)
history.append(population)
if i == epoch:
file = f'popsize{popsize}_epoch{epoch}_{ut.strnow("%Y%m%d_%H%M%S")}.pkl'
file = os.path.join(out, file)
if not os.path.exists(out):
os.makedirs(out)
print('save:', file)
ut.save(file, (env, optimizer, history))
return env, optimizer, history
def __init__(self, problem):
# パラメータ
n_dim = 30
pop_size = 100
ksize = 5 # MOEA/D近傍サイズ
# epoch = 250
# save_trigger = lambda i: i == 1 or i % 10 == 0 # 10 epochごと
# save_trigger = lambda i: i == epoch # 最後だけ
if problem == zdt4 or problem == zdt6:
n_dim = 10
if problem == zdt4:
Individual.set_bounds([0.0] + [-5.0] * (n_dim - 1),
[1.0] + [5.0] * (n_dim - 1))
# Individual.set_bounds([0], [1])
# Individual.set_weight([1, 1])
# 問題
# problem = zdt4
initializer = UniformInitializer(n_dim)
selection = RandomSelection()
# selection = TournamentSelection(ksize=2)
# selection = TournamentSelectionStrict(ksize=2)
# selection = TournamentSelectionDCD()
# crossover = BlendCrossover(alpha=0.5)
crossover = SimulatedBinaryCrossover(rate=0.9, eta=20)
mutation = PolynomialMutation(rate=1/n_dim, eta=20)
optimizer = MOEAD(pop_size, selection, crossover, mutation, ksize)
# optimizer.set_initializer(Initializer(3))
optimizer.setup(problem)
### Additional setting ###
optimizer.initializer = initializer
optimizer.n_cycle = None
optimizer.alternation = 'all'
##########################
self.optimizer = optimizer
def __init__(self, method, popsize=100, **kwargs):
method = method.lower()
if 'nsga' in method:
opt_cls = NSGA2
ga_ops = {}
elif 'moea' in method:
opt_cls = MOEAD
if kwargs['ksize']:
ksize = kwargs['ksize']
else:
ksize = 5
ga_ops = {'ksize': ksize}
else:
print('Unknown method name:', method)
raise RuntimeError
# 設計変数の次元数
n_dim = 3
# 設計範囲
low_bounds = [0, 0, 0]
upp_bounds = [1, 1, 1]
# 最適化重み(正=>最小化, 負=>最大化)
opt_weight = [1, 1]
# 問題関数
# problem = Problem()
problem = get_obj_func()
with Environment() as env:
# 個体クラス
indiv_pool = env.register(Individual)
# 遺伝子初期化クラス
initializer = UniformInitializer(n_dim)
# GAオペレータ指定
ga_ops = {
'selection': TournamentSelection(ksize=2),
'crossover': SimulatedBinaryCrossover(rate=0.9, eta=20),
'mutation': PolynomialMutation(rate=0.1, eta=20),
**ga_ops
}
# GAクラス
optimizer = opt_cls(problem=problem, pool=indiv_pool, **ga_ops)
### Additional setting ###
# 設計範囲設定
indiv_pool.cls.set_bounds(low_bounds, upp_bounds) # (下限，上限)
# 最適化重み付け(正=>最小化, 負=>最大化)
indiv_pool.cls.set_weight(opt_weight)
# 親個体選択時の初期化周期
optimizer.n_cycle = popsize // 2
##########################
# 個体生成器
creator = Creator(initializer, indiv_pool)
# 登録
env.optimizer = optimizer
env.creator = creator
self.env = env