saealib.ParetoComparator

class saealib.ParetoComparator(direction=None, *, eps_cv=1e-06, eps_obj=1e-06, sorter=<function non_dominated_sort>, dominator=None)

ベースクラス: Comparator

Comparator for multi-objective optimization via non-dominated sorting only.

Implements rank-only Pareto ordering:

• sort_population: Pareto front rank order (no crowding distance tiebreaking). Infeasible individuals (cv > eps) are always ranked after feasible ones, ordered by ascending constraint violation.

• compare_population: Pareto dominance (-1 = a dominates b, 1 = b dominates a, 0 = non-dominated). Infeasibility is handled with the standard constraint-domination rule.

NaN objective values are treated as non-dominating and placed last within the feasible block (consistent with non_dominated_sort).

This class is a concrete base for Pareto-based comparators. NSGA2Comparator inherits from it and overrides sort_population to add crowding-distance-based secondary ordering within each front.

パラメータ:

• direction (np.ndarray or None) -- Per-objective optimization directions (+1 = maximize, -1 = minimize). None means all objectives are minimized (standard Pareto dominance).

• eps_cv (float)

• eps_obj (float)

• sorter (NonDominatedSorter)

• dominator (Dominator | None)

Methods

__init__
compare	Compare two solutions directly without a Population object.
compare_population	Compare via Pareto dominance; -1=a dominates, 1=b dominates, 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 by Pareto front rank; infeasible individuals come last.

Method Details

ParetoComparator.__init__(direction=None, *, eps_cv=1e-06, eps_obj=1e-06, sorter=<function non_dominated_sort>, dominator=None)

パラメータ:

• direction (ndarray | None)

• eps_cv (float)

• eps_obj (float)

• sorter (NonDominatedSorter)

• dominator (Dominator | None)

ParetoComparator.compare(fa, cv_a, fb, cv_b)

Compare two solutions directly without a Population object.

パラメータ:

• fa (ndarray)

• cv_a (float)

• fb (ndarray)

• cv_b (float)

戻り値の型:

int

ParetoComparator.compare_population(population, idx_a, idx_b)

Compare via Pareto dominance; -1=a dominates, 1=b dominates, 0=equal.

パラメータ:

• population (Population)

• idx_a (int)

• idx_b (int)

戻り値の型:

int

ParetoComparator.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.

パラメータ:

problem (Problem) -- The optimization problem.

戻り値:

Empty by default.

戻り値の型:

list[PopulationAttribute]

ParetoComparator.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).

パラメータ:

population (Population) -- The population to prepare ranking state for.

戻り値の型:

None

ParetoComparator.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.

パラメータ:

population (Population) -- The population to rank.

戻り値:

Sorted population indices, as returned by sort_population().

戻り値の型:

np.ndarray

ParetoComparator.sort_population(population)

Sort by Pareto front rank; infeasible individuals come last.

パラメータ:

population (Population)

戻り値の型:

np.ndarray