Update MOEA/D

package/samplers/moead/README.md

@@ -2,7 +2,7 @@
 author: Hiroaki Natsume
 title: MOEA/D sampler
 description: Sampler using MOEA/D algorithm. MOEA/D stands for "Multi-Objective Evolutionary Algorithm based on Decomposition.
-tags: [sampler, multiobjective]
+tags: [Sampler, Multi-Objective Optimization, Evolutionary Algorithms]
 optuna_versions: [4.0.0]
 license: MIT License
 ---
@@ -13,6 +13,8 @@ Sampler using MOEA/D algorithm. MOEA/D stands for "Multi-Objective Evolutionary
 
 This sampler is specialized for multiobjective optimization. The objective function is internally decomposed into multiple single-objective subproblems to perform optimization.
 
+It may not work well with multi-threading. Check results carefully.
+
 ## Class or Function Names
 
 - MOEADSampler
@@ -23,6 +25,12 @@ This sampler is specialized for multiobjective optimization. The objective funct
 pip install scipy
 ```
 
+or
+
+```
+pip install -r https://hub.optuna.org/samplers/moead/requirements.txt
+```
+
 ## Example
 
 ```python
@@ -38,18 +46,17 @@ def objective(trial: optuna.Trial) -> tuple[float, float]:
     return v0, v1
 
 
-if __name__ == "__main__":
-    population_size = 100
-    n_trials = 1000
+population_size = 100
+n_trials = 1000
 
-    mod = optunahub.load_module("samplers/moead")
-    sampler = mod.MOEADSampler(
-        population_size=population_size,
-        scalar_aggregation_func="tchebycheff",
-        n_neighbors=population_size // 10,
-    )
-    study = optuna.create_study(sampler=sampler)
-    study.optimize(objective, n_trials=n_trials)
+mod = optunahub.load_module("samplers/moead")
+sampler = mod.MOEADSampler(
+    population_size=population_size,
+    scalar_aggregation_func="tchebycheff",
+    n_neighbors=population_size // 10,
+)
+study = optuna.create_study(sampler=sampler)
+study.optimize(objective, n_trials=n_trials)
 ```
 
 ## Others
@@ -71,4 +78,4 @@ See `compare_2objective.py` in moead directory for details.
 
 Q. Zhang and H. Li,
 "MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition," in IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, Dec. 2007,
-doi: 10.1109/TEVC.2007.892759.
+[doi: 10.1109/TEVC.2007.892759](https://doi.org/10.1109/TEVC.2007.892759).

package/samplers/moead/_child_generation_strategy.py

@@ -79,7 +79,7 @@ def __call__(
         parent_population: list[FrozenTrial],
         neighbors: dict[int, list[int]],
     ) -> dict[str, Any]:
-        """Generate a child parameter from the given parent population by NSGA-II algorithm.
+        """Generate a child parameter from the given parent population by MOEA/D algorithm.
         Args:
             study:
                 Target study object.
@@ -92,6 +92,7 @@ def __call__(
         Returns:
             A dictionary containing the parameter names and parameter's values.
         """
+        # TODO: this implementation might have unexpected behavior in the case of multi-threading.
         subproblem_parent_population = [
             parent_population[i] for i in neighbors[self._subproblem_id]
         ]

package/samplers/moead/moead.py

@@ -4,6 +4,7 @@
 import hashlib
 from typing import Any
 from typing import Dict
+from typing import Literal
 from typing import TYPE_CHECKING
 
 import optuna
@@ -34,7 +35,7 @@ def __init__(
         *,
         population_size: int = 100,
         n_neighbors: int | None = None,
-        scalar_aggregation_func: str = "tchebycheff",
+        scalar_aggregation_func: Literal["weighted_sum", "tchebycheff"] = "tchebycheff",
         mutation_prob: float | None = None,
         crossover: BaseCrossover | None = None,
         crossover_prob: float = 0.9,

package/samplers/moead/requirements.txt

@@ -0,0 +1 @@
+scipy==1.13.1