evomo.problems.constrained.dascmop#

Classes#

DASCMOP

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP1

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP2

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP3

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP4

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP5

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP6

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP7

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP8

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

DASCMOP9

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Module Contents#

class evomo.problems.constrained.dascmop.DASCMOP(d: int = None, m: int = None, n_iq: int = None, ref_num: int = 1000, difficulty: torch.Tensor = torch.tensor([0, 0.5, 0.5]), **kwargs)#

Bases: evomo.problems.constrained.base.CMOP

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

device#
ref_num = 1000#
m = None#
g1(X: torch.Tensor) → torch.Tensor#
g2(X: torch.Tensor) → torch.Tensor#
g3(X: torch.Tensor) → torch.Tensor#
class evomo.problems.constrained.dascmop.DASCMOP1(d=30, m=2, n_iq=11, ref_num=1000, difficulty=torch.tensor([0, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

constraints(X: torch.Tensor, g: torch.Tensor, f0: torch.Tensor | None = None, f1: torch.Tensor | None = None)#
fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP2(d=30, m=2, n_iq=11, ref_num=1000, difficulty=torch.tensor([0, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP1

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP3(difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP1

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP4(difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP1

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP5(difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP1

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP6(difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP1

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP7(d=30, m=3, n_iq=7, ref_num=1000, difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

constraints(X: torch.Tensor, g: torch.Tensor, f0: torch.Tensor, f1: torch.Tensor, f2: torch.Tensor) → torch.Tensor#
fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP8(d=30, m=3, n_iq=7, ref_num=1000, difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP7

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

objectives(X: torch.Tensor, g: torch.Tensor) → torch.Tensor#
fn(X: torch.Tensor) → torch.Tensor#
pf()#
class evomo.problems.constrained.dascmop.DASCMOP9(d=30, m=3, n_iq=7, ref_num=1000, difficulty=torch.tensor([0.5, 0.5, 0.5]), **kwargs)#

Bases: DASCMOP8

The DASCMOP test suite generator of constrained multibjecitve problems DASCMOP1-DASCMOP9.

Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, Difficulty adjustable and scalable constrained multi-objective test problem toolkit, Evolutionary Computation, 2020, 28(3): 339-378.

Parameters:

  • d – Number of decision variables.

  • m – Number of objectives.

  • n_iq – Number of inequality constraints.

  • n_eq – Number of equality constraints.

  • ref_num – Number of reference points used in the problem.

fn(X: torch.Tensor) → torch.Tensor#