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skills/pymoo/scripts/multi_objective_example.py

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+"""
+Multi-objective optimization example using pymoo.
+
+This script demonstrates multi-objective optimization using
+NSGA-II on the ZDT1 benchmark problem.
+"""
+
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.visualization.scatter import Scatter
+import matplotlib.pyplot as plt
+
+
+def run_multi_objective_optimization():
+    """Run multi-objective optimization example."""
+
+    # Define the problem - ZDT1 (bi-objective)
+    problem = get_problem("zdt1")
+
+    # Configure NSGA-II algorithm
+    algorithm = NSGA2(
+        pop_size=100,
+        eliminate_duplicates=True
+    )
+
+    # Run optimization
+    result = minimize(
+        problem,
+        algorithm,
+        ('n_gen', 200),
+        seed=1,
+        verbose=True
+    )
+
+    # Print results summary
+    print("\n" + "="*60)
+    print("MULTI-OBJECTIVE OPTIMIZATION RESULTS")
+    print("="*60)
+    print(f"Number of solutions in Pareto front: {len(result.F)}")
+    print(f"Number of generations: {result.algorithm.n_gen}")
+    print(f"Number of function evaluations: {result.algorithm.evaluator.n_eval}")
+    print("\nFirst 5 solutions (decision variables):")
+    print(result.X[:5])
+    print("\nFirst 5 solutions (objective values):")
+    print(result.F[:5])
+    print("="*60)
+
+    # Visualize results
+    plot = Scatter(title="ZDT1 - NSGA-II Results")
+    plot.add(result.F, color="red", alpha=0.7, s=30, label="Obtained Pareto Front")
+
+    # Add true Pareto front for comparison
+    pf = problem.pareto_front()
+    plot.add(pf, color="black", alpha=0.3, label="True Pareto Front")
+
+    plot.show()
+
+    return result
+
+
+if __name__ == "__main__":
+    result = run_multi_objective_optimization()