jmetal.problem.multiobjective.constrained.
Binh2
[source]¶Bases: jmetal.core.problem.FloatProblem
Class representing problem Binh2.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.constrained.
Osyczka2
[source]¶Bases: jmetal.core.problem.FloatProblem
Class representing problem Osyczka2.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.constrained.
Srinivas
[source]¶Bases: jmetal.core.problem.FloatProblem
Class representing problem Srinivas.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.constrained.
Tanaka
[source]¶Bases: jmetal.core.problem.FloatProblem
Class representing problem Tanaka.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.unconstrained.
Fonseca
[source]¶Bases: jmetal.core.problem.FloatProblem
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.unconstrained.
Kursawe
(number_of_variables: int = 3)[source]¶Bases: jmetal.core.problem.FloatProblem
Class representing problem Kursawe.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.unconstrained.
OneZeroMax
(number_of_bits: int = 256)[source]¶Bases: jmetal.core.problem.BinaryProblem
create_solution
() → jmetal.core.solution.BinarySolution[source]¶Creates a random_search solution to the problem.
Solution.
evaluate
(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.unconstrained.
Schaffer
[source]¶Bases: jmetal.core.problem.FloatProblem
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.unconstrained.
SubsetSum
(C: int, W: list)[source]¶Bases: jmetal.core.problem.BinaryProblem
create_solution
() → jmetal.core.solution.BinarySolution[source]¶Creates a random_search solution to the problem.
Solution.
evaluate
(solution: jmetal.core.solution.BinarySolution) → jmetal.core.solution.BinarySolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.unconstrained.
Viennet2
[source]¶Bases: jmetal.core.problem.FloatProblem
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.zdt.
ZDT1
(number_of_variables: int = 30)[source]¶Bases: jmetal.core.problem.FloatProblem
Problem ZDT1.
Note
Bi-objective unconstrained problem. The default number of variables is 30.
Note
Continuous problem having a convex Pareto front
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.zdt.
ZDT2
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.zdt.ZDT1
Problem ZDT2.
Note
Bi-objective unconstrained problem. The default number of variables is 30.
Note
Continuous problem having a non-convex Pareto front
jmetal.problem.multiobjective.zdt.
ZDT3
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.zdt.ZDT1
Problem ZDT3.
Note
Bi-objective unconstrained problem. The default number of variables is 30.
Note
Continuous problem having a partitioned Pareto front
jmetal.problem.multiobjective.zdt.
ZDT4
(number_of_variables: int = 10)[source]¶Bases: jmetal.problem.multiobjective.zdt.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
jmetal.problem.multiobjective.zdt.
ZDT6
(number_of_variables: int = 10)[source]¶Bases: jmetal.problem.multiobjective.zdt.ZDT1
Problem ZDT6.
Note
Bi-objective unconstrained problem. The default number of variables is 10.
Note
Continuous problem having a non-convex Pareto front
jmetal.problem.multiobjective.dtlz.
DTLZ1
(number_of_variables: int = 7, number_of_objectives=3)[source]¶Bases: jmetal.core.problem.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.dtlz.
DTLZ2
(number_of_variables: int = 12, number_of_objectives=3)[source]¶Bases: jmetal.problem.multiobjective.dtlz.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.dtlz.
DTLZ3
(number_of_variables: int = 12, number_of_objectives=3)[source]¶Bases: jmetal.problem.multiobjective.dtlz.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.dtlz.
DTLZ4
(number_of_variables: int = 12, number_of_objectives=3)[source]¶Bases: jmetal.problem.multiobjective.dtlz.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.dtlz.
DTLZ5
(number_of_variables: int = 12, number_of_objectives=3)[source]¶Bases: jmetal.problem.multiobjective.dtlz.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.dtlz.
DTLZ6
(number_of_variables: int = 12, number_of_objectives=3)[source]¶Bases: jmetal.problem.multiobjective.dtlz.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.dtlz.
DTLZ7
(number_of_variables: int = 22, number_of_objectives=3)[source]¶Bases: jmetal.problem.multiobjective.dtlz.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.fda.
FDA
[source]¶Bases: jmetal.core.problem.DynamicProblem
, jmetal.core.problem.FloatProblem
, abc.ABC
jmetal.problem.multiobjective.fda.
FDA1
(number_of_variables: int = 100)[source]¶Bases: jmetal.problem.multiobjective.fda.FDA
Problem FDA1.
Note
Bi-objective dynamic unconstrained problem. The default number of variables is 100.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.fda.
FDA2
(number_of_variables: int = 31)[source]¶Bases: jmetal.problem.multiobjective.fda.FDA
Problem FDA2
Note
Bi-objective dynamic unconstrained problem. The default number of variables is 31.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.fda.
FDA3
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.fda.FDA
Problem FDA3
Note
Bi-objective dynamic unconstrained problem. The default number of variables is 30.
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.fda.
FDA4
(number_of_variables: int = 12)[source]¶Bases: jmetal.problem.multiobjective.fda.FDA
Problem FDA4
Note
Three-objective dynamic unconstrained problem. The default number of variables is 12.
M
= 3¶evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.fda.
FDA5
(number_of_variables: int = 12)[source]¶Bases: jmetal.problem.multiobjective.fda.FDA
Problem FDA5
Note
Three-objective dynamic unconstrained problem. The default number of variables is 12.
M
= 3¶evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP1
(number_of_variables: int = 30)[source]¶Bases: jmetal.core.problem.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP10
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP11
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP12
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP13
(number_of_variables: int = 30)[source]¶Bases: jmetal.core.problem.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP14
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP2
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP3
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP4
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP5
(number_of_variables: int = 30)[source]¶Bases: jmetal.core.problem.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP6
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lircmop.
LIRCMOP7
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP8
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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
jmetal.problem.multiobjective.lircmop.
LIRCMOP9
(number_of_variables: int = 30)[source]¶Bases: jmetal.problem.multiobjective.lircmop.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: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.
jmetal.problem.multiobjective.lz09.
LZ09
(number_of_variables: int, number_of_objectives: int, number_of_constraints: int, ptype: int, dtype: int, ltype: int)[source]¶Bases: jmetal.core.problem.FloatProblem
evaluate
(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution[source]¶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.
Evaluated solution.