libmoon.problem
BaseMOP
- class BaseMOP
Base: object
This base class has the following functions.
- __init__(self, n_var, n_obj, lbound, ubound, n_cons=0)
Initialize the problem.
- Parameters:
n_var (int) – Number of variables.
n_obj (int) – Number of objectives.
lbound (ndarray) – The lower bound array.
ubound (ndarray) – The upper bound array.
n_cons (int) – Number of constraint functions.
- get_number_variable(self)
- Returns:
n_var.
- Return type:
int
- get_number_objective(self)
- Returns:
n_obj.
- Return type:
int
- get_number_constraint(self)
- Returns:
n_cons.
- Return type:
int
- get_lbound(self)
- Returns:
lbound.
- Return type:
ndarray
- get_ubound(self)
- Returns:
ubound.
- Return type:
ndarray
DTLZ
Synthetic DTLZ dataset from “Scalable test problems for evolutionary multiobjectiveoptimization, Evolutionary multiobjective Optimization.”
- class DTLZ1
Base:
BaseMOP- __init__(self, n_var=30, n_obj=3, lbound=np.zeros(30), ubound=np.ones(30))
Initialize the DTLZ1 problem.
- class DTLZ2
Base:
BaseMOP- __init__(self, n_var=30, n_obj=3, lbound=np.zeros(30), ubound=np.ones(30))
Initialize the DTLZ2 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
MAF1
Synthetic MAF1 dataset from “A benchmark test suite for evolutionary many-objective optimization.”
RE
Synthetic RE dataset from “An easy-to-use real-world multi-objective optimization problem suite.”
- class RE21
Base:
BaseMOP- __init__(self, n_var=4, n_obj=2, lbound=np.zeros(4), ubound=np.ones(4))
Initialize the RE21 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class RE22
Base:
BaseMOP- __init__(self, n_var=3, n_obj=2, lbound=np.zeros(3), ubound=np.ones(3))
Initialize the RE22 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class RE23
Base:
BaseMOP- __init__(self, n_var=4, n_obj=2, lbound=np.zeros(4), ubound=np.ones(4))
Initialize the RE23 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class RE24
Base:
BaseMOP- __init__(self, n_var=2, n_obj=2, lbound=np.zeros(2), ubound=np.ones(2))
Initialize the RE24 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class RE25
Base:
BaseMOP- __init__(self, n_var=3, n_obj=2, lbound=np.zeros(3), ubound=np.ones(3))
Initialize the RE25 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class RE31
Base:
BaseMOP- __init__(self, n_var=3, n_obj=3, lbound=np.zeros(3), ubound=np.ones(3))
Initialize the RE31 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class RE37
Base:
BaseMOP- __init__(self, n_var=3, n_obj=3, lbound=np.zeros(4), ubound=np.ones(3))
Initialize the RE37 problem.
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
VLMOP
Synthetic VLMOP dataset from “Distributed Multiobjective Optimization Problems and Methods for their Solution.”
ZDT
Synthetic ZDT dataset from “An easy-to-use real-world multi-objective optimization problem suite”
- class ZDT1
Base:
BaseMOP- __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), ubound=np.ones(30)
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class ZDT2
Base:
BaseMOP- __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), ubound=np.ones(30)
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)
- class ZDT3
Base:
BaseMOP- __init__(self, n_var=30, n_obj=2, lbound=np.zeros(30), ubound=np.ones(30)
- _evaluate_torch(self, x)
- _evaluate_numpy(self, x)