evomo.problems.constrained.lscm

Classes

LSCM
LSCM1
LSCM2
LSCM3
LSCM4
LSCM5
LSCM6
LSCM7
LSCM8
LSCM9
LSCM10
LSCM11
LSCM12

Functions

duan_yueshu(→ Tuple[list, list, int, int, list, int, int])	Returns:
PF_function(→ torch.Tensor)

Module Contents

evomo.problems.constrained.lscm.duan_yueshu(index: int) → Tuple[list, list, int, int, list, int, int]

Returns:

nk: list, number of variables in each segment c_index: list, indexes of CEC_problem used for each segment (mapped) CCT: int, 1=linear, 2=nonlinear (cos) DCT: int, 1=linear, 2=nonlinear (cos) Dis_function: list, two distance function IDs [a, b] (used alternately per segment) PF_form: int, 1=linear, 2=convex, 3=disconnected Q_form: int, 1=single, 2=multiple, 3=full

evomo.problems.constrained.lscm.PF_function(PopDec: torch.Tensor, G: torch.Tensor, PF_form: int, Q_form: int) → torch.Tensor

class evomo.problems.constrained.lscm.LSCM(ku_index: int, d: int = 100, m: int = 2, ref_num: int = 1000, device: torch.device | None = None, dtype: torch.dtype | None = None, **kwargs)

Bases: evomo.problems.constrained.base.CMOP

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

m = 2

ref_num = 1000

base = 100

d = 100

duan = 1

nk = [5]

CEC_Problem

lu: List[torch.Tensor] = [None]

high_D_C: List[int] = [0]

aaa: List[torch.Tensor | None] = [None]

optimal_f: List[torch.Tensor] = [None]

sublen: List[torch.Tensor] = [None]

len: List[torch.Tensor] = [None]

CV: List[torch.Tensor] = [None]

Dis: List[Dict[str, torch.Tensor]] = [None]

variable_information(c: torch.Tensor)

Dis{i,j}{h},i indicates the index of obj.duan, j indicates the index of objectives,h indicates the index of nk

variable_linkage(X: torch.Tensor) → torch.Tensor

fn(X: torch.Tensor) → torch.Tensor

pf()

class evomo.problems.constrained.lscm.LSCM1(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM2(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM3(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM4(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM5(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM6(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM7(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM8(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM9(d: int = 100, m: int = 2, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM10(d: int = 100, m: int = 3, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM11(d: int = 100, m: int = 3, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function

class evomo.problems.constrained.lscm.LSCM12(d: int = 100, m: int = 3, ref_num: int = 1000, **kwargs)

Bases: LSCM

K. Qiao, J. Liang, K. Yu, W. Guo, C. Yue, B. Qu, and P. N. Suganthan. Benchmark problems for large-scale constrained multi-objective optimization with baseline results. Swarm and Evolutionary Computation, 2024, 86: 101504.

ku_index = 1: index of LSCM function base: the dimension of each segment duan: the number of segments DCT: the form of unconstraint variable linkage CCT: the form of constraint variable linkage Dis_function: index of distance functions Q_form: index of Q functions PF_form: index of PF functions CEC_Problem: index of constraint functions lu: upper and lower bounds of constraint functions high_D_C: dimensions of constraint functions aaa: used for constraint functions optimal_f: optimal objective values of constraint functions

nk: Number of subcomponents in each subset sublen: Number of variables in each subset len: Cumulative sum of lengths of variable groups

CV: store the index of constaint variables Dis: store the index of distance function variables

h: objective function value of constraint function HC: constraint function value of constraint function