evomo.problems.numerical.zdt

Classes

ZDT	The ZDT test suite for multiobjective optimization.
ZDT1	The ZDT test suite for multiobjective optimization.
ZDT2	The ZDT test suite for multiobjective optimization.
ZDT3	The ZDT test suite for multiobjective optimization.
ZDT4	The ZDT test suite for multiobjective optimization.
ZDT6	The ZDT test suite for multiobjective optimization.

Module Contents

class evomo.problems.numerical.zdt.ZDT(n: int, ref_num: int = 100)

Bases: evox.core.Problem

The ZDT test suite for multiobjective optimization.

This suite of benchmark functions is commonly used to evaluate the performance of multiobjective evolutionary algorithms.

References:

[1] E. Zitzler, K. Deb, and L. Thiele, “Comparison of Multiobjective Evolutionary Algorithms:

Empirical Results,” in Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000. Available: https://ieeexplore.ieee.org/abstract/document/6787994

Initialize the ZDT problem instance.

Parameters:

• n – The dimensionality of the problem (number of variables).

• ref_num – The reference number for evaluating the problem, default is 100.

n

ref_num = 100

evaluate(X: torch.Tensor)

pf()

class evomo.problems.numerical.zdt.ZDT1(n)

Bases: ZDT

The ZDT test suite for multiobjective optimization.

This suite of benchmark functions is commonly used to evaluate the performance of multiobjective evolutionary algorithms.

References:

[1] E. Zitzler, K. Deb, and L. Thiele, “Comparison of Multiobjective Evolutionary Algorithms:

Empirical Results,” in Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000. Available: https://ieeexplore.ieee.org/abstract/document/6787994

Initialize the ZDT problem instance.

Parameters:

• n – The dimensionality of the problem (number of variables).

• ref_num – The reference number for evaluating the problem, default is 100.

class evomo.problems.numerical.zdt.ZDT2(n)

Bases: ZDT

The ZDT test suite for multiobjective optimization.

This suite of benchmark functions is commonly used to evaluate the performance of multiobjective evolutionary algorithms.

References:

[1] E. Zitzler, K. Deb, and L. Thiele, “Comparison of Multiobjective Evolutionary Algorithms:

Empirical Results,” in Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000. Available: https://ieeexplore.ieee.org/abstract/document/6787994

Initialize the ZDT problem instance.

Parameters:

• n – The dimensionality of the problem (number of variables).

• ref_num – The reference number for evaluating the problem, default is 100.

pf()

class evomo.problems.numerical.zdt.ZDT3(n)

Bases: ZDT

The ZDT test suite for multiobjective optimization.

This suite of benchmark functions is commonly used to evaluate the performance of multiobjective evolutionary algorithms.

References:

[1] E. Zitzler, K. Deb, and L. Thiele, “Comparison of Multiobjective Evolutionary Algorithms:

Empirical Results,” in Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000. Available: https://ieeexplore.ieee.org/abstract/document/6787994

Initialize the ZDT problem instance.

Parameters:

• n – The dimensionality of the problem (number of variables).

• ref_num – The reference number for evaluating the problem, default is 100.

pf()

class evomo.problems.numerical.zdt.ZDT4(n)

Bases: ZDT

The ZDT test suite for multiobjective optimization.

This suite of benchmark functions is commonly used to evaluate the performance of multiobjective evolutionary algorithms.

References:

[1] E. Zitzler, K. Deb, and L. Thiele, “Comparison of Multiobjective Evolutionary Algorithms:

Empirical Results,” in Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000. Available: https://ieeexplore.ieee.org/abstract/document/6787994

Initialize the ZDT problem instance.

Parameters:

• n – The dimensionality of the problem (number of variables).

• ref_num – The reference number for evaluating the problem, default is 100.

class evomo.problems.numerical.zdt.ZDT6(n)

Bases: ZDT

The ZDT test suite for multiobjective optimization.

This suite of benchmark functions is commonly used to evaluate the performance of multiobjective evolutionary algorithms.

References:

[1] E. Zitzler, K. Deb, and L. Thiele, “Comparison of Multiobjective Evolutionary Algorithms:

Empirical Results,” in Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000. Available: https://ieeexplore.ieee.org/abstract/document/6787994

Initialize the ZDT problem instance.

Parameters:

• n – The dimensionality of the problem (number of variables).

• ref_num – The reference number for evaluating the problem, default is 100.

pf()