evomo.problems.numerical.maf#

Classes#

MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF1

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF2

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF3

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF4

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF5

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF6

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF7

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF8

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF9

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF10

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF11

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF12

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF13

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF14

The MAF benchmark test suite for evolutionary many-objective optimization.

MAF15

The MAF benchmark test suite for evolutionary many-objective optimization.

Module Contents#

class evomo.problems.numerical.maf.MAF(d: int = None, m: int = 3, ref_num: int = 1000, device: torch.device | None = None)#

Bases: evox.core.Problem

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

device#
m = 3#
d = 12#
ref_num = 1000#
evaluate(X: torch.Tensor)#
pf()#
class evomo.problems.numerical.maf.MAF1(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF2(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF3(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF4(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF5(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF6(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF7(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF8(d=2, m=10, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

points#
evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF9(d=2, m=10, ref_num=1000, device: torch.device | None = None)#

Bases: MAF8

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF10(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF11(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF10

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

class evomo.problems.numerical.maf.MAF12(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF13(d=5, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF14(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

sublen#
len#
evaluate(X: torch.Tensor)#
class evomo.problems.numerical.maf.MAF15(d=None, m=3, ref_num=1000, device: torch.device | None = None)#

Bases: MAF14

The MAF benchmark test suite for evolutionary many-objective optimization.

References:
[1] R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao, “A benchmark test suite

for evolutionary many-objective optimization,” Complex & Intelligent Systems, vol. 3, no. 1, pp. 67-81, 2017. Available: https://link.springer.com/article/10.1007/s40747-017-0039-7

Initialize the MAF problem instance.

Parameters:

  • d – The dimensionality of the problem (number of decision variables). If None, it is set to m + 9.

  • m – The number of objectives for the problem. Default is 3.

  • ref_num – The reference number for evaluating the problem, default is 1000.

  • device – The device to perform computations on (e.g., CPU or GPU). If None, the default device is used.

evaluate(X: torch.Tensor)#