moead_framework.core.sps_strategy.sps_dra.SpsDra

class moead_framework.core.sps_strategy.sps_dra.SpsDra(algorithm_instance)

Bases: moead_framework.core.sps_strategy.abstract_sps.SpsStrategy

The strategy used in MOEA/D-DRA.

Q. Zhang, W. Liu and H. Li, “The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances” 2009 IEEE Congress on Evolutionary Computation Trondheim, 2009, pp. 203-208 doi: 10.1109/CEC.2009.4982949.

The strategy requires the attribute pi in the algorithm to stock all utility values of each sub-problems. This attribute is a list with the same size as the number of available sub-problems.

__init__(algorithm_instance)

Constructor of the Sub-Problem Selection Strategy

Parameters

algorithm_instance – {AbstractMoead} instance of the algorithm

Methods

__init__(algorithm_instance)	Constructor of the Sub-Problem Selection Strategy
get_sub_problems()	Select at first the indexes of the sub problems whose objectives are MOP individual objectives fi (i.e.
get_xtrem_index()	get boundaries sub-problems

get_sub_problems()

Select at first the indexes of the sub problems whose objectives are MOP individual objectives fi (i.e. boundaries sub-problems) and add sub-problems with a 10-tournament

Returns

{list<integer>} indexes of sub-problems

get_xtrem_index()

get boundaries sub-problems

Returns

{list<integer>} indexes of sub-problems