Consolidate skills

scientific-skills/pymoo/scripts/single_objective_example.py

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+"""
+Single-objective optimization example using pymoo.
+
+This script demonstrates basic single-objective optimization
+using the Genetic Algorithm on the Sphere function.
+"""
+
+from pymoo.algorithms.soo.nonconvex.ga import GA
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.operators.crossover.sbx import SBX
+from pymoo.operators.mutation.pm import PM
+from pymoo.operators.sampling.rnd import FloatRandomSampling
+from pymoo.termination import get_termination
+import numpy as np
+
+
+def run_single_objective_optimization():
+    """Run single-objective optimization example."""
+
+    # Define the problem - Sphere function (sum of squares)
+    problem = get_problem("sphere", n_var=10)
+
+    # Configure the algorithm
+    algorithm = GA(
+        pop_size=100,
+        sampling=FloatRandomSampling(),
+        crossover=SBX(prob=0.9, eta=15),
+        mutation=PM(eta=20),
+        eliminate_duplicates=True
+    )
+
+    # Define termination criteria
+    termination = get_termination("n_gen", 100)
+
+    # Run optimization
+    result = minimize(
+        problem,
+        algorithm,
+        termination,
+        seed=1,
+        verbose=True
+    )
+
+    # Print results
+    print("\n" + "="*60)
+    print("OPTIMIZATION RESULTS")
+    print("="*60)
+    print(f"Best solution: {result.X}")
+    print(f"Best objective value: {result.F[0]:.6f}")
+    print(f"Number of generations: {result.algorithm.n_gen}")
+    print(f"Number of function evaluations: {result.algorithm.evaluator.n_eval}")
+    print("="*60)
+
+    return result
+
+
+if __name__ == "__main__":
+    result = run_single_objective_optimization()