Add example for AGE-MOEA-II with constrained problems

examples/algorithms/moo/age2_constrained.py

@@ -0,0 +1,64 @@
+from pymoo.indicators.igd import IGD
+from pymoo.util.ref_dirs import get_reference_directions
+from pymoo.algorithms.moo.age2 import AGEMOEA2
+from pymoo.optimize import minimize
+
+from pymoo.problems.many import C1DTLZ1, DC1DTLZ1, DC1DTLZ3, DC2DTLZ1, DC2DTLZ3, DC3DTLZ1, DC3DTLZ3, C1DTLZ3, \
+    C2DTLZ2, C3DTLZ1, C3DTLZ4
+import ray
+import numpy as np
+
+benchmark_algorithms = [
+    AGEMOEA2(),
+]
+
+benchmark_problems = [
+    C1DTLZ1, DC1DTLZ1, DC1DTLZ3, DC2DTLZ1, DC2DTLZ3, DC3DTLZ1, DC3DTLZ3, C1DTLZ3, C2DTLZ2, C3DTLZ1, C3DTLZ4
+]
+
+
+def run_benchmark(problem_class, algorithm):
+    # Instantiate the problem
+    problem = problem_class()
+
+    res = minimize(
+        problem,
+        algorithm,
+        pop_size=100,
+        verbose=True,
+        seed=1,
+        termination=('n_gen', 2000)
+    )
+
+    # Step 4: Generate the reference points
+    ref_dirs = get_reference_directions("uniform", problem.n_obj, n_points=528)
+
+    # Obtain the true Pareto front (for synthetic problems)
+    pareto_front = problem.pareto_front(ref_dirs)
+
+    # Calculate IGD
+    if res.F is None:
+        igd = np.Infinity
+    else:
+        igd = IGD(pareto_front)(res.F)
+
+    result = {
+        "problem": problem,
+        "algorithm": algorithm,
+        "result": res,
+        "igd": igd
+    }
+
+    return result
+
+
+tasks = []
+for problem in benchmark_problems:
+    for algorithm in benchmark_algorithms:
+        tasks.append(ray.remote(run_benchmark).remote(problem, algorithm))
+result = ray.get(tasks)
+
+for res in result:
+    print(f"Algorithm = {res['algorithm'].__class__.__name__}, "
+          f"Problem = {res['problem'].__class__.__name__}, "
+          f"IGD = {res['igd']}")