Add lexicographic Pareto front selection example

docs/source/misc/lexicographic_pareto_selection.rst

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+Lexicographic Selection from a Pareto Front
+==========================================
+
+In multi-objective optimization, the result is often not a single solution, but a set of Pareto-optimal solutions.
+This set represents trade-offs between conflicting objectives. However, in practical decision-making, one final
+solution is often required.
+
+One simple way to choose a final solution is lexicographic ordering. In this approach, objectives are sorted by
+importance. First, solutions are compared by the most important objective. If several solutions are equal or almost
+equal, the next objective is used, and so on.
+
+The example below shows a small helper function for selecting one solution from an already obtained Pareto front.
+All objectives are assumed to be minimized.
+
+.. code-block:: python
+
+    import numpy as np
+
+    def lexicographic_selection(F, priority_order=None):
+        F = np.asarray(F)
+
+        if priority_order is None:
+            priority_order = list(range(F.shape[1]))
+
+        order = np.lexsort(tuple(F[:, i] for i in reversed(priority_order)))
+        return order[0]
+
+    F = np.array([
+        [0.10, 0.80],
+        [0.20, 0.40],
+        [0.15, 0.55],
+        [0.10, 0.90],
+    ])
+
+    selected = lexicographic_selection(F, priority_order=[0, 1])
+
+    print("Selected solution index:", selected)
+    print("Selected objective values:", F[selected])
+
+In this example, the first objective has the highest priority. The function first compares solutions by the first
+objective and then uses the second objective to break ties. This can be useful when a user already has a Pareto front
+and needs to select one final compromise solution according to predefined preferences.
+
+If some objectives should be maximized, they can be multiplied by -1 before applying the function.