Merge branch 'anyoptimization:main' into reconstruct_objs

docs/source/algorithms/algorithms.csv

@@ -16,4 +16,4 @@
    :ref:`AGE-MOEA <nb_agemoea>`, AGEMOEA, many,, Similar to NSGA-II but estimates the shape of the Pareto-front to compute a score replacing the crowding distance.
    :ref:`C-TAEA <nb_ctaea>`, CTAEA, many, x, An algorithm with a more sophisticated constraint-handling for many-objective optimization algoritms.
    :ref:`SMS-EMOA <nb_sms>`, CTAEA, many, x, An algorithm that uses hypervolume during the environmental survival.
-   :ref:`RVEA <nb_rvea>`, RVEA, many, x, A reference direction based algorithm used an angle-penalized metric.
+   :ref:`RVEA <nb_rvea>`, RVEA, many, , A reference direction based algorithm used an angle-penalized metric.

pymoo/constraints/adaptive.py

@@ -40,7 +40,7 @@ def __init__(self, algorithm):
     def _setup(self, _, **kwargs):
         self.evaluator = AttachConfigEvaluator(self.evaluator, self.config)
 
-    def _adapt(self, config, infills=None, **kwargs):
+    def _adapt_constraint_handling(self, config, infills=None, **kwargs):
         pass
 
     def _initialize_advance(self, infills=None, **kwargs):
@@ -53,7 +53,7 @@ def _advance(self, infills=None, **kwargs):
         super()._advance(infills=infills, **kwargs)
         copy_to_dict(self.default_config, self.config)
 
-        self._adapt(self.adapted_config, infills=infills, **kwargs)
+        self._adapt_constraint_handling(self.adapted_config, infills=infills, **kwargs)
 
     def _infill(self):
         copy_to_dict(self.adapted_config, self.config)

pymoo/constraints/eps.py

@@ -10,7 +10,7 @@ def __init__(self, algorithm, perc_eps_until=0.5):
         self.perc_eps_until = perc_eps_until
         self.max_cv = None
 
-    def _adapt(self, config, **kwargs):
+    def _adapt_constraint_handling(self, config, **kwargs):
         t = self.termination.perc
         alpha = np.maximum(0.0, 1 - 1 / self.perc_eps_until * t)
         eps = alpha * self.max_cv

pymoo/core/algorithm.py

@@ -256,6 +256,7 @@ def result(self):
 
         res.pop = self.pop
         res.archive = self.archive
+        res.data = self.data
 
         # get the optimal solution found
         opt = self.opt

pymoo/core/result.py

@@ -36,6 +36,9 @@ def __init__(self) -> None:
         # the history of the optimization run is they were saved
         self.history = []
 
+        # data stored within the algorithm
+        self.data = None
+
     @property
     def cv(self):
         return self.CV[0]