Consolidate skills

scientific-skills/pymoo/scripts/many_objective_example.py

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+"""
+Many-objective optimization example using pymoo.
+
+This script demonstrates many-objective optimization (4+ objectives)
+using NSGA-III on the DTLZ2 benchmark problem.
+"""
+
+from pymoo.algorithms.moo.nsga3 import NSGA3
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.util.ref_dirs import get_reference_directions
+from pymoo.visualization.pcp import PCP
+import numpy as np
+
+
+def run_many_objective_optimization():
+    """Run many-objective optimization example."""
+
+    # Define the problem - DTLZ2 with 5 objectives
+    n_obj = 5
+    problem = get_problem("dtlz2", n_obj=n_obj)
+
+    # Generate reference directions for NSGA-III
+    # Das-Dennis method for uniform distribution
+    ref_dirs = get_reference_directions("das-dennis", n_obj, n_partitions=12)
+
+    print(f"Number of reference directions: {len(ref_dirs)}")
+
+    # Configure NSGA-III algorithm
+    algorithm = NSGA3(
+        ref_dirs=ref_dirs,
+        eliminate_duplicates=True
+    )
+
+    # Run optimization
+    result = minimize(
+        problem,
+        algorithm,
+        ('n_gen', 300),
+        seed=1,
+        verbose=True
+    )
+
+    # Print results summary
+    print("\n" + "="*60)
+    print("MANY-OBJECTIVE OPTIMIZATION RESULTS")
+    print("="*60)
+    print(f"Number of objectives: {n_obj}")
+    print(f"Number of solutions: {len(result.F)}")
+    print(f"Number of generations: {result.algorithm.n_gen}")
+    print(f"Number of function evaluations: {result.algorithm.evaluator.n_eval}")
+
+    # Show objective space statistics
+    print("\nObjective space statistics:")
+    print(f"Minimum values per objective: {result.F.min(axis=0)}")
+    print(f"Maximum values per objective: {result.F.max(axis=0)}")
+    print("="*60)
+
+    # Visualize using Parallel Coordinate Plot
+    plot = PCP(
+        title=f"DTLZ2 ({n_obj} objectives) - NSGA-III Results",
+        labels=[f"f{i+1}" for i in range(n_obj)],
+        normalize_each_axis=True
+    )
+    plot.add(result.F, alpha=0.3, color="blue")
+    plot.show()
+
+    return result
+
+
+if __name__ == "__main__":
+    result = run_many_objective_optimization()