Multiobjective problems

Constrained

class jmetal.problem.multiobjective.constrained.Srinivas(rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Class representing problem Srinivas.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → None

get_name()

class jmetal.problem.multiobjective.constrained.Tanaka(rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Class representing problem Tanaka

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

evaluate_constraints(solution: jmetal.core.solution.FloatSolution) → None

get_name()

Unconstrained

class jmetal.problem.multiobjective.unconstrained.Fonseca(rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.unconstrained.Kursawe(number_of_variables: int = 3, rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Class representing problem Kursawe.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.unconstrained.Schaffer(rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.unconstrained.Viennet2(rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

DTLZ

class jmetal.problem.multiobjective.dtlz.DTLZ1(number_of_variables: int = 7, number_of_objectives=3, rf_path: str = None)

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.

Parameters:

• number_of_variables – number of decision variables of the problem.
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.dtlz.DTLZ2(number_of_variables: int = 12, number_of_objectives=3, rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Problem DTLZ2. Continuous problem having a convex Pareto front

Note

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

Parameters:

• number_of_variables – number of decision variables of the problem
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

ZDT

class jmetal.problem.multiobjective.zdt.ZDT1(number_of_variables: int = 30, rf_path: str = None)

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

Parameters:

• number_of_variables – Number of decision variables of the problem.
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.zdt.ZDT2(number_of_variables: int = 30, rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Problem ZDT2.

Note

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

Note

Continuous problem having a non-convex Pareto front

Parameters:

• number_of_variables – Number of decision variables of the problem.
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.zdt.ZDT3(number_of_variables: int = 30, rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Problem ZDT3.

Note

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

Note

Continuous problem having a partitioned Pareto front

Parameters:

• number_of_variables – Number of decision variables of the problem.
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.zdt.ZDT4(number_of_variables: int = 10, rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Problem ZDT4.

Note

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

Note

Continuous multi-modal problem having a convex Pareto front

Parameters:

• number_of_variables – Number of decision variables of the problem.
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()

class jmetal.problem.multiobjective.zdt.ZDT6(number_of_variables: int = 10, rf_path: str = None)

Bases: jmetal.core.problem.FloatProblem

Problem ZDT6.

Note

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

Note

Continuous problem having a non-convex Pareto front

Parameters:

• number_of_variables – Number of decision variables of the problem.
• rf_path – Path to the reference front file (if any). Default to None.

evaluate(solution: jmetal.core.solution.FloatSolution) → jmetal.core.solution.FloatSolution

get_name()