desdeo.method.NIMBUS

class desdeo.method.NIMBUS(problem, method_class)

Bases: desdeo.method.base.InteractiveMethod

‘ Abstract class for optimization methods

_preference

Preference, i.e., classification information information for current iteration

Type

ClNIMBUSClassificationdefault:None)

__init__(problem, method_class)

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

Methods

__init__(problem, method_class)	Initialize self.
between(objs1, objs2[, n])	Generate n solutions which attempt to trade-off objs1 and objs2.
init_iteration(*args, **kwargs)	Return the initial solution(s)
next_iteration(*args, **kwargs)	Generate the next iteration’s solutions using the DM’s preferences and the NIMBUS scalarization functions.

_NIMBUS__SCALARS = ['NIM', 'ACH', 'GUESS', 'STOM']

__abstractmethods__ = frozenset({})

__init__(problem, method_class)

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

__module__ = 'desdeo.method.NIMBUS'

_get_ach()

_init_iteration(*args, **kwargs)

Return type

ResultSet

_next_iteration(*args, **kwargs)

Return type

ResultSet

between(objs1, objs2, n=1)

Generate n solutions which attempt to trade-off objs1 and objs2.

Parameters

• objs1 (List[float]) – First boundary point for desired objective function values

• objs2 (List[float]) – Second boundary point for desired objective function values

• n – Number of solutions to generate

next_iteration(*args, **kwargs)

Generate the next iteration’s solutions using the DM’s preferences and the NIMBUS scalarization functions.

Parameters

• preference (NIMBUSClassification) – Preference classifications obtained from the DM

• scalars (list of strings) – List containing one or more of the scalarizing functions: NIM, ACH, GUESS, STOM

• num_scalars (number) – The number of scalarizing functions to use (mutually exclusive with scalars)