desdeo.problem¶

This package contains tools for modelling multi-objective optimisation problems.

class desdeo.problem.PythonProblem(nobj, nconst=0, ideal=None, nadir=None, maximized=None, objectives=None, name=None, points=None)[source]¶

Bases: desdeo.problem.Problem.MOProblem

__module__ = 'desdeo.problem.Problem'¶

class desdeo.problem.PreGeneratedProblem(filename=None, points=None, delim=', ', **kwargs)[source]¶

Bases: desdeo.problem.Problem.MOProblem

A problem where the objective function values have beeen pregenerated

__init__(filename=None, points=None, delim=', ', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

__module__ = 'desdeo.problem.Problem'¶

evaluate(population=None)[source]¶

Evaluate the objective and constraint functions for population and return tuple (objective,constraint) values

Parameters: population (list of variable values) – Description

class desdeo.problem.Variable(bounds=None, starting_point=None, name='')[source]¶

Bases: object

bounds¶

lower and upper boundaries of the variable

Type: list of numeric values

name¶

Name of the variable

Type: string

starting_point¶

Starting point for the variable

Type: numeric value

__dict__ = mappingproxy({'__module__': 'desdeo.problem.Problem', '__doc__': '\n Attributes\n ----------\n bounds : list of numeric values\n lower and upper boundaries of the variable\n\n name : string\n Name of the variable\n\n starting_point : numeric value\n Starting point for the variable\n ', '__init__': <function Variable.__init__>, '__dict__': <attribute '__dict__' of 'Variable' objects>, '__weakref__': <attribute '__weakref__' of 'Variable' objects>})¶

__init__(bounds=None, starting_point=None, name='')[source]¶: Constructor

__module__ = 'desdeo.problem.Problem'¶

__weakref__¶: list of weak references to the object (if defined)

class desdeo.problem.MOProblem(nobj, nconst=0, ideal=None, nadir=None, maximized=None, objectives=None, name=None, points=None)[source]¶

Bases: object

Abstract base class for multiobjective problem

variables¶

MOProblem decision variable information

Type: list of Variables

ideal¶: Ideal, i.e, the worst values of objective functions

nadir¶: Nadir, i.e, the best values of objective functions

maximized¶: Indicates maximized objectives

__dict__ = mappingproxy({'__module__': 'desdeo.problem.Problem', '__doc__': '\n Abstract base class for multiobjective problem\n\n Attributes\n ----------\n variables : list of Variables\n MOProblem decision variable information\n\n ideal\n Ideal, i.e, the worst values of objective functions\n\n nadir\n Nadir, i.e, the best values of objective functions\n\n maximized\n Indicates maximized objectives\n ', '__metaclass__': <class 'abc.ABCMeta'>, '__init__': <function MOProblem.__init__>, 'evaluate': <function MOProblem.evaluate>, 'objective_bounds': <function MOProblem.objective_bounds>, 'nof_objectives': <function MOProblem.nof_objectives>, 'nof_variables': <function MOProblem.nof_variables>, 'add_variables': <function MOProblem.add_variables>, 'as_minimized': <function MOProblem.as_minimized>, 'bounds': <function MOProblem.bounds>, '__dict__': <attribute '__dict__' of 'MOProblem' objects>, '__weakref__': <attribute '__weakref__' of 'MOProblem' objects>})¶

__init__(nobj, nconst=0, ideal=None, nadir=None, maximized=None, objectives=None, name=None, points=None)[source]¶

Initialize self. See help(type(self)) for accurate signature.

Return type: None

__metaclass__¶: alias of abc.ABCMeta

__module__ = 'desdeo.problem.Problem'¶

__weakref__¶: list of weak references to the object (if defined)

add_variables(variables, index=None)[source]¶

Parameters

variable (list of variables or single variable) – Add variables as problem variables
index (int) – Location to add variables, if None add to the end

Return type

None

as_minimized(v)[source]¶

bounds()[source]¶

evaluate(population)[source]¶

Evaluate the objective and constraint functions for population and return tuple (objective,constraint) values

Parameters: population (list of variable values) – Description

nof_objectives()[source]¶

Return type: Optional[int]

nof_variables()[source]¶

Return type: int

objective_bounds()[source]¶

Return objective bounds

Returns

lower (list of floats) – Lower boundaries for the objectives
Upper (list of floats) – Upper boundaries for the objectives