Multi-objective problems

Constrained

class jmetal.problem.multiobjective.constrained.Binh2

Bases: FloatProblem

Class representing problem Binh2.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.constrained.Osyczka2

Bases: FloatProblem

Class representing problem Osyczka2.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.constrained.Srinivas

Bases: FloatProblem

Class representing problem Srinivas.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.constrained.Tanaka

Bases: FloatProblem

Class representing problem Tanaka.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

Unconstrained

class jmetal.problem.multiobjective.unconstrained.Fonseca

Bases: FloatProblem

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.unconstrained.Kursawe(number_of_variables: int = 3)

Bases: FloatProblem

Class representing problem Kursawe.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.unconstrained.MixedIntegerFloatProblem(number_of_integer_variables=10, number_of_float_variables=10, n=100, m=-100, lower_bound=-1000, upper_bound=1000)

Bases: Problem

create_solution() → CompositeSolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: CompositeSolution) → CompositeSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

class jmetal.problem.multiobjective.unconstrained.OneZeroMax(number_of_bits: int = 256)

Bases: BinaryProblem

The implementation of the OneZeroMax problems defines a single binary variable. This variable will contain the bit string representing the solutions.

create_solution() → BinarySolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: BinarySolution) → BinarySolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

class jmetal.problem.multiobjective.unconstrained.Schaffer

Bases: FloatProblem

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.unconstrained.SubsetSum(C: int, W: list)

Bases: BinaryProblem

create_solution() → BinarySolution

Creates a random_search solution to the problem.

Returns:

Solution.

evaluate(solution: BinarySolution) → BinarySolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name() → str

class jmetal.problem.multiobjective.unconstrained.Viennet2

Bases: FloatProblem

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

ZDT

class jmetal.problem.multiobjective.zdt.ZDT1(number_of_variables: int = 30)

Bases: FloatProblem

Problem ZDT1.

Note

Bi-objective unconstrained problem. The default number of variables is 30.

Note

Continuous problem having a convex Pareto front

eval_g(solution: FloatSolution)

eval_h(f: float, g: float) → float

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

class jmetal.problem.multiobjective.zdt.ZDT1Modified(number_of_variables=30)

Bases: ZDT1

Problem ZDT1Modified.

Note

Version including a loop for increasing the computing time of the evaluation functions.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

class jmetal.problem.multiobjective.zdt.ZDT2(number_of_variables: int = 30)

Bases: ZDT1

Problem ZDT2.

Note

Bi-objective unconstrained problem. The default number of variables is 30.

Note

Continuous problem having a non-convex Pareto front

eval_h(f: float, g: float) → float

name()

class jmetal.problem.multiobjective.zdt.ZDT3(number_of_variables: int = 30)

Bases: ZDT1

Problem ZDT3.

Note

Bi-objective unconstrained problem. The default number of variables is 30.

Note

Continuous problem having a partitioned Pareto front

eval_h(f: float, g: float) → float

name()

class jmetal.problem.multiobjective.zdt.ZDT4(number_of_variables: int = 10)

Bases: ZDT1

Problem ZDT4.

Note

Bi-objective unconstrained problem. The default number of variables is 10.

Note

Continuous multi-modal problem having a convex Pareto front

eval_g(solution: FloatSolution)

eval_h(f: float, g: float) → float

name()

class jmetal.problem.multiobjective.zdt.ZDT5(number_of_variables: int = 11)

Bases: BinaryProblem

Problem ZDT5.

Note

Bi-objective binary unconstrained problem. The default number of variables is 11.

create_solution() → BinarySolution

Creates a random_search solution to the problem.

Returns:

Solution.

eval_g(solution: BinarySolution)

eval_v(value)

evaluate(solution: BinarySolution) → BinarySolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

class jmetal.problem.multiobjective.zdt.ZDT6(number_of_variables: int = 10)

Bases: ZDT1

Problem ZDT6.

Note

Bi-objective unconstrained problem. The default number of variables is 10.

Note

Continuous problem having a non-convex Pareto front

eval_g(solution: FloatSolution)

eval_h(f: float, g: float) → float

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

DTLZ

class jmetal.problem.multiobjective.dtlz.DTLZ1(number_of_variables: int = 7, number_of_objectives=3)

Bases: FloatProblem

Problem DTLZ1. Continuous problem having a flat Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 7 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

number_of_constraints() → int

number_of_objectives() → int

number_of_variables() → int

class jmetal.problem.multiobjective.dtlz.DTLZ2(number_of_variables: int = 12, number_of_objectives=3)

Bases: DTLZ1

Problem DTLZ2. Continuous problem having a convex Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

class jmetal.problem.multiobjective.dtlz.DTLZ3(number_of_variables: int = 12, number_of_objectives=3)

Bases: DTLZ1

Problem DTLZ3. Continuous problem having a convex Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

class jmetal.problem.multiobjective.dtlz.DTLZ4(number_of_variables: int = 12, number_of_objectives=3)

Bases: DTLZ1

Problem DTLZ4. Continuous problem having a convex Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

class jmetal.problem.multiobjective.dtlz.DTLZ5(number_of_variables: int = 12, number_of_objectives=3)

Bases: DTLZ1

Problem DTLZ5. Continuous problem having a convex Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

class jmetal.problem.multiobjective.dtlz.DTLZ6(number_of_variables: int = 12, number_of_objectives=3)

Bases: DTLZ1

Problem DTLZ6. Continuous problem having a convex Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 12 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

class jmetal.problem.multiobjective.dtlz.DTLZ7(number_of_variables: int = 22, number_of_objectives=3)

Bases: DTLZ1

Problem DTLZ6. Continuous problem having a disconnected Pareto front

Note

Unconstrained problem. The default number of variables and objectives are, respectively, 22 and 3.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

FDA

class jmetal.problem.multiobjective.fda.FDA

Bases: DynamicProblem, FloatProblem, ABC

clear_changed() → None

abstract evaluate(solution: FloatSolution)

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

the_problem_has_changed() → bool

update(*args, **kwargs)

Update method.

class jmetal.problem.multiobjective.fda.FDA1(number_of_variables: int = 100)

Bases: FDA

Problem FDA1.

Note

Bi-objective dynamic unconstrained problem. The default number of variables is 100.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

get_name()

class jmetal.problem.multiobjective.fda.FDA2(number_of_variables: int = 31)

Bases: FDA

Problem FDA2

Note

Bi-objective dynamic unconstrained problem. The default number of variables is 31.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

get_name()

class jmetal.problem.multiobjective.fda.FDA3(number_of_variables: int = 30)

Bases: FDA

Problem FDA3

Note

Bi-objective dynamic unconstrained problem. The default number of variables is 30.

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

get_name()

class jmetal.problem.multiobjective.fda.FDA4(number_of_variables: int = 12)

Bases: FDA

Problem FDA4

Note

Three-objective dynamic unconstrained problem. The default number of variables is 12.

M = 3

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

get_name()

class jmetal.problem.multiobjective.fda.FDA5(number_of_variables: int = 12)

Bases: FDA

Problem FDA5

Note

Three-objective dynamic unconstrained problem. The default number of variables is 12.

M = 3

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

get_name()

LIRCMOP

class jmetal.problem.multiobjective.lircmop.LIRCMOP1(number_of_variables: int = 30)

Bases: FloatProblem

Class representing problem LIR-CMOP1, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

evaluate_constraints(solution: FloatSolution) → FloatSolution

g1(x: List[float]) → float

g2(x: List[float]) → float

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.lircmop.LIRCMOP10(number_of_variables: int = 30)

Bases: LIRCMOP8

Class representing problem LIR-CMOP10, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP11(number_of_variables: int = 30)

Bases: LIRCMOP10

Class representing problem LIR-CMOP11, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP12(number_of_variables: int = 30)

Bases: LIRCMOP9

Class representing problem LIR-CMOP9, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP13(number_of_variables: int = 30)

Bases: FloatProblem

Class representing problem LIR-CMOP13, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

evaluate_constraints(solution: FloatSolution) → FloatSolution

g1(x: [<class 'float'>]) → float

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.lircmop.LIRCMOP14(number_of_variables: int = 30)

Bases: LIRCMOP13

Class representing problem LIR-CMOP14, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.lircmop.LIRCMOP2(number_of_variables: int = 30)

Bases: LIRCMOP1

Class representing problem LIR-CMOP1, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP3(number_of_variables: int = 30)

Bases: LIRCMOP1

Class representing problem LIR-CMOP3, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

number_of_constraints() → int

class jmetal.problem.multiobjective.lircmop.LIRCMOP4(number_of_variables: int = 30)

Bases: LIRCMOP2

Class representing problem LIR-CMOP4, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

number_of_constraints() → int

class jmetal.problem.multiobjective.lircmop.LIRCMOP5(number_of_variables: int = 30)

Bases: FloatProblem

Class representing problem LIR-CMOP5, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

evaluate_constraints(solution: FloatSolution) → FloatSolution

g1(x: [<class 'float'>]) → float

g2(x: [<class 'float'>]) → float

name()

number_of_constraints() → int

number_of_objectives() → int

class jmetal.problem.multiobjective.lircmop.LIRCMOP6(number_of_variables: int = 30)

Bases: LIRCMOP5

Class representing problem LIR-CMOP6, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP7(number_of_variables: int = 30)

Bases: LIRCMOP5

Class representing problem LIR-CMOP7, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP8(number_of_variables: int = 30)

Bases: LIRCMOP6

Class representing problem LIR-CMOP8, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

class jmetal.problem.multiobjective.lircmop.LIRCMOP9(number_of_variables: int = 30)

Bases: LIRCMOP8

Class representing problem LIR-CMOP9, defined in:

• An Improved epsilon-constrained Method in MOEA/D for CMOPs with Large Infeasible Regions. Fan, Z., Li, W., Cai, X. et al. Soft Comput (2019). https://doi.org/10.1007/s00500-019-03794-x

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

evaluate_constraints(solution: FloatSolution) → FloatSolution

name()

LZ09

class jmetal.problem.multiobjective.lz09.LZ09(number_of_variables: int, ptype: int, dtype: int, ltype: int)

Bases: FloatProblem

evaluate(solution: FloatSolution) → FloatSolution

Evaluate a solution. For any new problem inheriting from Problem, this method should be replaced. Note that this framework ASSUMES minimization, thus solutions must be evaluated in consequence.

Returns:

Evaluated solution.

get_name()

number_of_constraints() → int

objective(x_variables: list) → list

class jmetal.problem.multiobjective.lz09.LZ09_F1(number_of_variables=10)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F2(number_of_variables=30)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F3(number_of_variables=30)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F4(number_of_variables=30)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F5(number_of_variables=30)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F6(number_of_variables=10)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F7(number_of_variables=10)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F8(number_of_variables=10)

Bases: LZ09

name()

number_of_objectives() → int

class jmetal.problem.multiobjective.lz09.LZ09_F9(number_of_variables=30)

Bases: LZ09

name()

number_of_objectives() → int