Merge remote-tracking branch 'origin/master' into develop

.github/workflows/develop.yml

@@ -13,7 +13,7 @@ jobs:
       - name: Install Python 3
         uses: actions/setup-python@v2
         with:
-          python-version: 3.9
+          python-version: 3.10
       - name: Install Dependencies
         run: |
           python -m pip install --upgrade pip

.github/workflows/release.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Install Python 3
         uses: actions/setup-python@v2
         with:
-          python-version: 3.9
+          python-version: 3.10
       - name: Install Dependencies
         run: |
           python -m pip install --upgrade pip
@@ -36,12 +36,14 @@ jobs:
      strategy:
        matrix:
          os: [ubuntu-latest, windows-latest, macos-latest]
-         python-version: ['3.7', '3.8', '3.9']
+         python-version: ['3.7', '3.8', '3.9', '3.10']
          exclude:
            - os: ubuntu-latest
              python-version: '3.7'
            - os: ubuntu-latest
              python-version: '3.8'
+           - os: ubuntu-latest
+               python-version: '3.9'
      steps:
      - uses: actions/checkout@v2
      - name: Set up Python
@@ -70,7 +72,7 @@ jobs:
     - uses: actions/checkout@v2
     - uses: RalfG/[email protected]_x86_64
       with:
-        python-versions: 'cp37-cp37m cp38-cp38 cp39-cp39'
+        python-versions: 'cp37-cp37m cp38-cp38 cp39-cp39 cp310-cp310'
         build-requirements: 'cython numpy'
         pip-wheel-args: '-w ./dist --no-deps'
     - name: Remove non-compatible packages
@@ -93,7 +95,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
-         python-version: '3.9'
+         python-version: '3.10'
      - name: Publish
        env:
          TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}

.gitignore

@@ -2,6 +2,7 @@
 **/.vscode
 **/.mp4
 **/.npy
+**/.gif
 
 
 # Byte-compiled / optimized / DLL files

README.rst

@@ -86,7 +86,7 @@ However, for instance, executing NSGA2:
 
 
     from pymoo.algorithms.moo.nsga2 import NSGA2
-    from pymoo.factory import get_problem
+    from pymoo.problems import get_problem
     from pymoo.optimize import minimize
     from pymoo.visualization.scatter import Scatter
 

examples/algorithms/ask_and_tell.py

@@ -0,0 +1,28 @@
+from pymoo.algorithms.soo.nonconvex.pso import PSO
+from pymoo.core.problem import Problem
+from pymoo.core.termination import NoTermination
+from pymoo.problems.static import StaticProblem
+
+problem = Problem(n_var=10, n_obj=1, n_ieq_constr=0, xl=-0, xu=1)
+
+algorithm = PSO().setup(problem, termination=NoTermination(), verbose=False)
+
+for k in range(20):
+
+    if not algorithm.has_next():
+        break
+
+    infills = algorithm.ask()
+
+    X = infills.get("X")
+
+    F = (X ** 2).sum(axis=1)
+    G = - (X[:, 0] + X[:, 1]) - 0.3
+
+    algorithm.evaluator.eval(StaticProblem(problem, F=F, G=G), infills)
+
+    algorithm.tell(infills=infills)
+
+    print(k + 1, algorithm.opt[0].F[0])
+
+print(algorithm.opt.get("F"))

examples/algorithms/callback.py

@@ -0,0 +1,35 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from pymoo.algorithms.soo.nonconvex.ga import GA
+from pymoo.problems import get_problem
+from pymoo.core.callback import Callback
+from pymoo.optimize import minimize
+
+
+class MyCallback(Callback):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.data["best"] = []
+
+    def update(self, algorithm):
+        self.data["best"].append(algorithm.pop.get("F").min())
+
+
+problem = get_problem("sphere")
+
+algorithm = GA(pop_size=100)
+
+res = minimize(problem,
+               algorithm,
+               ('n_gen', 20),
+               seed=1,
+               callback=MyCallback(),
+               save_history=True,
+               verbose=True)
+
+val = res.algorithm.callback.data["best"]
+plt.plot(np.arange(len(val)), val)
+plt.show()
+

examples/algorithms/custom_output.py

@@ -0,0 +1,35 @@
+import numpy as np
+
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.util.display.column import Column
+from pymoo.util.display.output import Output
+
+
+class MyOutput(Output):
+
+    def __init__(self):
+        super().__init__()
+        self.columns["x-mean"] = Column(13)
+        self.columns["x-std"] = Column(13)
+        self.active += ["x-mean", "x-std"]
+
+    def update(self, algorithm):
+        super().update(algorithm)
+
+        X = algorithm.pop.get("X")
+        self.columns["x-mean"].value = np.mean(X)
+        self.columns["x-std"].value = np.std(X)
+
+
+problem = get_problem("zdt2")
+
+algorithm = NSGA2(pop_size=100)
+
+res = minimize(problem,
+               algorithm,
+               ('n_gen', 10),
+               seed=1,
+               output=MyOutput(),
+               verbose=True)

examples/algorithms/custom_output_lambda.py

@@ -0,0 +1,29 @@
+import numpy as np
+
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.util.display.column import Column
+from pymoo.util.display.output import Output
+
+
+class MyOutput(Output):
+
+    def __init__(self):
+        super().__init__()
+        self.x_mean = Column("x_mean", width=13, func=lambda algorithm: np.mean(algorithm.pop.get("X")))
+        self.x_std = Column("x_std", width=13, func=lambda algorithm: np.std(algorithm.pop.get("X")))
+        self.columns += [self.x_mean, self.x_std]
+
+problem = get_problem("zdt2")
+
+algorithm = NSGA2(pop_size=100)
+
+res = minimize(problem,
+               algorithm,
+               ('n_gen', 10),
+               seed=1,
+               output=MyOutput(),
+               verbose=True)
+
+

examples/algorithms/initial_array.py

@@ -0,0 +1,17 @@
+import numpy as np
+
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+
+problem = get_problem("zdt2")
+
+X = np.random.random((300, problem.n_var))
+
+algorithm = NSGA2(pop_size=100, sampling=X)
+
+minimize(problem,
+         algorithm,
+         ('n_gen', 10),
+         seed=1,
+         verbose=True)

examples/algorithms/initial_evaluated.py

@@ -0,0 +1,24 @@
+import numpy as np
+
+from pymoo.algorithms.soo.nonconvex.ga import GA
+from pymoo.problems import get_problem
+from pymoo.core.evaluator import Evaluator
+from pymoo.core.population import Population
+from pymoo.optimize import minimize
+
+problem = get_problem("sphere")
+
+X = np.random.random((500, problem.n_var))
+
+pop = Population.empty(len(X))
+pop.set("X", X)
+Evaluator().eval(problem, pop)
+
+algorithm = GA(sampling=pop)
+
+res = minimize(problem,
+               algorithm,
+               seed=1,
+               verbose=True)
+
+print("Best solution found: \nX = %s\nF = %s" % (res.X, res.F))

examples/algorithms/initial_pop.py

@@ -0,0 +1,22 @@
+import numpy as np
+
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.core.evaluator import Evaluator
+from pymoo.core.population import Population
+from pymoo.optimize import minimize
+
+problem = get_problem("zdt2")
+
+# create initial data and set to the population object
+X = np.random.random((300, problem.n_var))
+pop = Population.new("X", X)
+Evaluator().eval(problem, pop)
+
+algorithm = NSGA2(pop_size=100, sampling=pop)
+
+minimize(problem,
+         algorithm,
+         ('n_gen', 10),
+         seed=1,
+         verbose=True)

examples/algorithms/loop.py

@@ -0,0 +1,20 @@
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.termination import get_termination
+from pymoo.visualization.scatter import Scatter
+
+problem = get_problem("zdt1")
+
+algorithm = NSGA2(pop_size=100)
+
+algorithm.setup(problem, termination=get_termination('n_gen', 200), verbose=True)
+
+while algorithm.has_next():
+    algorithm.next()
+
+res = algorithm.result()
+
+plot = Scatter()
+plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
+plot.add(res.opt.get("F"), color="red")
+plot.show()

examples/algorithms/moo/age.py

@@ -0,0 +1,20 @@
+from pymoo.algorithms.moo.age import AGEMOEA
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.visualization.scatter import Scatter
+
+problem = get_problem("tnk")
+
+algorithm = AGEMOEA()
+
+res = minimize(problem,
+               algorithm,
+               ('n_gen', 200),
+               seed=1,
+               verbose=True)
+
+plot = Scatter()
+plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
+plot.add(res.F, color="red")
+plot.show()
+

examples/algorithms/moo/age2.py

@@ -0,0 +1,21 @@
+from pymoo.algorithms.moo.age import AGEMOEA
+from pymoo.algorithms.moo.age2 import AGEMOEA2
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.visualization.scatter import Scatter
+
+problem = get_problem("zdt1")
+
+algorithm = AGEMOEA2()
+
+res = minimize(problem,
+               algorithm,
+               ('n_gen', 200),
+               seed=1,
+               verbose=True)
+
+plot = Scatter()
+plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
+plot.add(res.F, color="red")
+plot.show()
+

examples/algorithms/moo/ctaea.py

@@ -0,0 +1,21 @@
+from pymoo.algorithms.moo.ctaea import CTAEA
+
+from pymoo.optimize import minimize
+from pymoo.problems.multi import Carside
+from pymoo.util.ref_dirs import get_reference_directions
+from pymoo.visualization.scatter import Scatter
+
+problem = Carside()
+
+ref_dirs = get_reference_directions("das-dennis", 3, n_partitions=12)
+
+algorithm = CTAEA(ref_dirs=ref_dirs)
+
+res = minimize(problem,
+               algorithm,
+               ('n_gen', 600),
+               seed=1,
+               verbose=True
+               )
+
+Scatter().add(res.F, facecolor="none", edgecolor="red").show()

examples/algorithms/moo/moead.py

@@ -0,0 +1,21 @@
+from pymoo.algorithms.moo.moead import MOEAD, ParallelMOEAD
+from pymoo.optimize import minimize
+from pymoo.problems import get_problem
+from pymoo.util.ref_dirs import get_reference_directions
+from pymoo.visualization.scatter import Scatter
+
+problem = get_problem("dtlz2")
+
+ref_dirs = get_reference_directions("das-dennis", 3, n_partitions=12)
+
+algorithm = MOEAD(
+    ref_dirs,
+    n_neighbors=15,
+    prob_neighbor_mating=0.7,
+    seed=1,
+    verbose=False
+)
+
+res = minimize(problem, algorithm, termination=('n_gen', 200), verbose=True)
+
+Scatter().add(res.F).show()

examples/algorithms/moo/nsga2/nsga2.py

@@ -0,0 +1,19 @@
+from pymoo.algorithms.moo.nsga2 import NSGA2
+from pymoo.problems import get_problem
+from pymoo.optimize import minimize
+from pymoo.visualization.scatter import Scatter
+
+problem = get_problem("bnh")
+
+algorithm = NSGA2(pop_size=100)
+
+res = minimize(problem,
+               algorithm,
+               seed=1,
+               verbose=True)
+
+plot = Scatter()
+plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
+plot.add(res.F, facecolor="none", edgecolor="red")
+plot.show()
+