desdeo.problem

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

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

Bases: abc.ABC

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

__abstractmethods__ = frozenset({'evaluate'})

__init__(nobj, nconst=0, ideal=None, nadir=None, maximized=None, objectives=None, name=None, points=None)

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

Return type

None

__module__ = 'desdeo.problem.Problem'

add_variables(variables, index=None)

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)

bounds()

abstract evaluate(population)

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

Parameters

population (list of variable values) – Description

nof_objectives()

Return type

Optional[int]

nof_variables()

Return type

int

objective_bounds()

Return objective bounds

Returns

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

• Upper (list of floats) – Upper boundaries for the objectives

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

Bases: desdeo.problem.Problem.MOProblem

A problem where the objective function values have beeen pregenerated

__abstractmethods__ = frozenset({})

__init__(filename=None, points=None, delim=',', **kwargs)

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

__module__ = 'desdeo.problem.Problem'

evaluate(population=None)

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

Parameters

population (list of variable values) – Description

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

Bases: desdeo.problem.Problem.MOProblem

__abstractmethods__ = frozenset({'evaluate'})

__module__ = 'desdeo.problem.Problem'

class desdeo.problem.Variable(bounds=None, starting_point=None, name='')

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='')

Constructor

__module__ = 'desdeo.problem.Problem'

__weakref__

list of weak references to the object (if defined)