saealib.WeightedSumComparator#

class saealib.WeightedSumComparator(direction=None, *, eps_cv=1e-06, eps_obj=1e-06)[source]#

Bases: Comparator

Comparator for multi-objective optimization via weighted scalarization.

Aggregates multiple objectives into a single scalar using a weighted dot product: score = f @ weights. The sign convention for weights follows the same pattern as SingleObjectiveComparator: use negative weights for minimization (e.g., weights=np.array([-1.0, -1.0])).

Also supports single-objective problems (n_obj == 1).

Parameters:

direction (np.ndarray) – Per-objective optimization directions (±1). shape = (n_obj,)
eps_cv (float)
eps_obj (float)

Methods

`__init__`
`compare`	Compare two solutions directly without a Population object.
`compare_population`	Compare two individuals by weighted sum; -1=a better, 1=b better, 0=equal.
`get_required_attrs`	Return population attributes this comparator needs to persist.
`prepare_population`	Freshly recompute and materialize population-relative ranking state.
`rank_population`	Prepare population-relative ranking state, then sort.
`sort_population`	Sort population by weighted sum of objectives, with feasibility first.

Method Details

WeightedSumComparator.__init__(direction=None, *, eps_cv=1e-06, eps_obj=1e-06)[source]#

Parameters:

direction (ndarray | None)
eps_cv (float)
eps_obj (float)

WeightedSumComparator.compare(fa, cv_a, fb, cv_b)[source]#

Compare two solutions directly without a Population object.

Parameters:

fa (ndarray)
cv_a (float)
fb (ndarray)
cv_b (float)

Return type:

int

WeightedSumComparator.compare_population(population, idx_a, idx_b)[source]#

Compare two individuals by weighted sum; -1=a better, 1=b better, 0=equal.

Parameters:

population (Population)
idx_a (int)
idx_b (int)

Return type:

int

WeightedSumComparator.get_required_attrs(problem)#

Return population attributes this comparator needs to persist.

Population-relative ranking state (e.g. SPEA2 fitness) that must survive across selections belongs here as a named, typed attribute rather than in the population’s transient cache. Default: no additional attributes.

Parameters:: problem (Problem) – The optimization problem.
Returns:: Empty by default.
Return type:: list[PopulationAttribute]

WeightedSumComparator.prepare_population(population)#

Freshly recompute and materialize population-relative ranking state.

Default is a no-op — comparators whose ranking depends only on per-individual values already stored in f/cv do not need this. Comparators that declare attributes via get_required_attrs() should override this to recompute and write those attributes for the given population, unconditionally (not reusing any prior cached value).

Parameters:: population (Population) – The population to prepare ranking state for.
Return type:: None

WeightedSumComparator.rank_population(population)#

Prepare population-relative ranking state, then sort.

Equivalent to calling prepare_population() followed by sort_population(). Callers performing environmental selection on a freshly assembled pool (e.g. survivor selection merging population and offspring) should call this instead of sort_population() directly, so any population-relative state declared via get_required_attrs() is recomputed for the new set before ranking.

Parameters:: population (Population) – The population to rank.
Returns:: Sorted population indices, as returned by sort_population().
Return type:: np.ndarray

WeightedSumComparator.sort_population(population)[source]#

Sort population by weighted sum of objectives, with feasibility first.

Parameters:: population (Population)
Return type:: np.ndarray