desdeo.method¶
This package contains methods for interactively solving multi-objective optimisation problems. Currently this includes the NIMBUS methods and several variants of the NAUTILUS method.
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class
desdeo.method.
ENAUTILUS
(method, method_class)[source]¶ Bases:
desdeo.method.NAUTILUS.NAUTILUS
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__abstractmethods__
= frozenset({})¶
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__init__
(method, method_class)[source]¶ Initialize self. See help(type(self)) for accurate signature.
- Return type
None
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__module__
= 'desdeo.method.NAUTILUS'¶
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class
desdeo.method.
NAUTILUSv1
(method, method_class)[source]¶ Bases:
desdeo.method.NAUTILUS.NAUTILUS
The first NAUTILUS method variant [MIETTINEN2010].
References
- MIETTINEN2010
Miettinen, K.; Eskelinen, P.; Ruiz, F. & Luque, M., NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point, European Journal of Operational Research, 2010 , 206 , 426-434.
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__abstractmethods__
= frozenset({})¶
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__init__
(method, method_class)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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__module__
= 'desdeo.method.NAUTILUS'¶
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class
desdeo.method.
NIMBUS
(problem, method_class)[source]¶ Bases:
desdeo.method.base.InteractiveMethod
‘ Abstract class for optimization methods
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_preference
¶ Preference, i.e., classification information information for current iteration
- Type
ClNIMBUSClassificationdefault:None)
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_NIMBUS__SCALARS
= ['NIM', 'ACH', 'GUESS', 'STOM']¶
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__abstractmethods__
= frozenset({})¶
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__init__
(problem, method_class)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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__module__
= 'desdeo.method.NIMBUS'¶
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between
(objs1, objs2, n=1)[source]¶ Generate n solutions which attempt to trade-off objs1 and objs2.
- Parameters
objs1 (
List
[float
]) – First boundary point for desired objective function valuesobjs2 (
List
[float
]) – Second boundary point for desired objective function valuesn – Number of solutions to generate
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next_iteration
(*args, **kwargs)[source]¶ 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)
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class
desdeo.method.
NNAUTILUS
(method, method_class)[source]¶ Bases:
desdeo.method.NAUTILUS.NAUTILUS
NAVIGATOR NAUTILUS method
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fh
¶ Current non-dominated point
- Type
list of floats
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zh
¶ Current iteration point
- Type
list of floats
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fh_up
¶ Upper boundary for iteration points reachable from iteration point zh
- Type
list of floats
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fh_lo
¶ Lower boundary for iteration points reachable from iteration point zh
- Type
list of floats
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exception
NegativeIntervalWarning
[source]¶ Bases:
desdeo.utils.warnings.UnexpectedCondition
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__module__
= 'desdeo.method.NAUTILUS'¶
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__abstractmethods__
= frozenset({})¶
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__init__
(method, method_class)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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__module__
= 'desdeo.method.NAUTILUS'¶
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