feat: implement next-gen vectorized engine, async architecture, and modernization roadmap v2.32.0

core_py/optimizer.py

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+import numpy as np
+import random
+from typing import Callable, Dict, Any
+
+class ParameterOptimizer:
+    """
+    지능형 파라미터 최적화 엔진 (Algorithmic Review 1.2 반영)
+    브루트 포스 대신 시뮬레이티드 어닐링 또는 경사 하강 초기화를 활용함.
+    """
+
+    def __init__(self, objective_function: Callable):
+        self.objective_function = objective_function
+
+    def simulated_annealing(self, initial_params: np.ndarray, iterations: int = 1000, temp: float = 1.0, cooling_rate: float = 0.95):
+        """
+        시뮬레이티드 어닐링(Simulated Annealing) 기반 최적화
+        지역 최적점(Local Optima) 탈출이 가능하며 브루트 포스보다 압도적으로 빠름.
+        """
+        current_params = initial_params
+        current_score = self.objective_function(current_params)
+        
+        best_params = current_params
+        best_score = current_score
+        
+        for i in range(iterations):
+            # 이웃 해(Neighbor) 탐색
+            neighbor_params = current_params + np.random.normal(0, 0.1, size=current_params.shape)
+            neighbor_score = self.objective_function(neighbor_params)
+            
+            # 수락 확률 계산 (Metropolis Criterion)
+            if neighbor_score > current_score or random.random() < np.exp((neighbor_score - current_score) / temp):
+                current_params = neighbor_params
+                current_score = neighbor_score
+                
+                if current_score > best_score:
+                    best_score = current_score
+                    best_params = neighbor_params
+            
+            # 냉각 (Cooling)
+            temp *= cooling_rate
+            
+        print(f"[Optimizer] Best Score Found: {best_score:.4f}")
+        return best_params
+
+# Example Objective Function (e.g., Accuracy based on threshold and weights)
+def dummy_objective(params):
+    # 가상의 성능 평가 함수 (파라미터가 0.5에 가까울수록 높은 점수)
+    return -np.sum((params - 0.5)**2)
+
+if __name__ == "__main__":
+    optimizer = ParameterOptimizer(dummy_objective)
+    initial = np.array([0.1, 0.9, 0.2])
+    print(f"Starting Intelligent Optimization from {initial}...")
+    best = optimizer.simulated_annealing(initial)
+    print(f"Optimized Parameters: {best}")