Updates documentation

.github/workflows/develop.yml

@@ -38,7 +38,7 @@ jobs:
           sudo apt-get install pandoc rsync
           python -m pip install --upgrade pip
           pip install -r doc/requirements.txt
-          pip install numpy matplotlib autograd
+          pip install numpy matplotlib autograd scipy
       - name: Create Documentation
         run: |
           cd doc

pymoo/algorithms/moo/moead.py

@@ -10,7 +10,6 @@
 from pymoo.operators.mutation.polynomial_mutation import PolynomialMutation
 from pymoo.operators.sampling.random_sampling import FloatRandomSampling
 from pymoo.util.display import MultiObjectiveDisplay
-from pymoo.util.misc import parameter_less
 
 
 # =========================================================================================================
@@ -59,9 +58,6 @@ def __init__(self,
                  display=MultiObjectiveDisplay(),
                  **kwargs):
         """
-
-        MOEAD Algorithm.
-
         Parameters
         ----------
         ref_dirs

pymoo/algorithms/moo/nsga2.py

@@ -212,3 +212,5 @@ def calc_crowding_distance(F, filter_out_duplicates=True):
 
 
 parse_doc_string(NSGA2.__init__)
+
+print("sdfs")

pymoo/algorithms/soo/convex/nonderiv/pattern_search.py → pymoo/algorithms/soo/nonconvex/pattern_search.py

@@ -5,11 +5,9 @@
 from pymoo.model.individual import Individual
 from pymoo.model.population import Population
 from pymoo.model.replacement import is_better
-from pymoo.model.termination import Termination
 from pymoo.operators.repair.to_bound import set_to_bounds_if_outside_by_problem
 from pymoo.util.display import SingleObjectiveDisplay
 from pymoo.util.optimum import filter_optimum
-from pymoo.util.termination.default import SingleObjectiveDefaultTermination
 
 
 # =========================================================================================================

pymoo/factory.py

@@ -65,10 +65,10 @@ def get_algorithm_options():
     from pymoo.algorithms.soo.nonconvex.de import DE
     from pymoo.algorithms.soo.nonconvex.ga import GA
     from pymoo.algorithms.moo.unsga3 import UNSGA3
-    from pymoo.algorithms.soo.convex.nonderiv.nelder_mead import NelderMead
-    from pymoo.algorithms.soo.convex.nonderiv.cmaes import CMAES
+    from pymoo.algorithms.soo.nonconvex.nelder_mead import NelderMead
+    from pymoo.algorithms.soo.nonconvex.cmaes import CMAES
     from pymoo.algorithms.soo.nonconvex.brkga import BRKGA
-    from pymoo.algorithms.soo.convex.nonderiv.pattern_search import PatternSearch
+    from pymoo.algorithms.soo.nonconvex.pattern_search import PatternSearch
 
     ALGORITHMS = [
         ("ga", GA),

pymoo/operators/crossover/nelder_mead_crossover.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from pymoo.algorithms.soo.convex.nonderiv.nelder_mead import max_alpha
+from pymoo.algorithms.soo.nonconvex.nelder_mead import max_alpha
 from pymoo.model.crossover import Crossover
 
 

pymoo/usage/algorithms/usage_ask_and_tell.py

@@ -1,4 +1,4 @@
-from pymoo.algorithms.soo.convex.nonderiv.cmaes import CMAES
+from pymoo.algorithms.soo.nonconvex.cmaes import CMAES
 from pymoo.factory import Problem
 from pymoo.model.evaluator import Evaluator, set_cv
 

pymoo/usage/algorithms/usage_cmaes.py

@@ -1,10 +1,10 @@
 import numpy as np
 
-from pymoo.algorithms.soo.convex.nonderiv.cmaes import CMAES
-from pymoo.factory import Sphere
+from pymoo.algorithms.soo.nonconvex.cmaes import CMAES
+from pymoo.factory import get_problem
 from pymoo.optimize import minimize
 
-problem = Sphere()
+problem = get_problem("sphere")
 
 algorithm = CMAES(x0=np.random.random(problem.n_var))
 

pymoo/usage/algorithms/usage_moead.py

@@ -12,6 +12,7 @@
     decomposition="pbi",
     prob_neighbor_mating=0.7,
     seed=1,
+    verbose=False
 )
 
 res = minimize(problem, algorithm, termination=('n_gen', 200), verbose=True)

pymoo/usage/algorithms/usage_nelder_mead.py

@@ -1,4 +1,4 @@
-from pymoo.algorithms.soo.convex.nonderiv.nelder_mead import NelderMead
+from pymoo.algorithms.soo.nonconvex.nelder_mead import NelderMead
 from pymoo.factory import get_problem
 from pymoo.optimize import minimize
 

pymoo/usage/algorithms/usage_pattern_search.py

@@ -1,4 +1,4 @@
-from pymoo.algorithms.soo.convex.nonderiv.pattern_search import PatternSearch
+from pymoo.algorithms.soo.nonconvex.pattern_search import PatternSearch
 from pymoo.factory import Himmelblau
 from pymoo.optimize import minimize
 

tests/algorithms/test_nelder_mead.py

@@ -1,7 +1,7 @@
 import numpy as np
 from scipy.optimize import minimize as scipy_minimize
 
-from pymoo.algorithms.soo.convex.nonderiv.nelder_mead import NelderMead
+from pymoo.algorithms.soo.nonconvex.nelder_mead import NelderMead
 from pymoo.factory import get_problem, get_termination
 from pymoo.optimize import minimize
 

tests/algorithms/test_no_error.py

@@ -1,16 +1,15 @@
 import pytest
 
-from pymoo.algorithms.moo.gde3 import GDE3
 from pymoo.algorithms.moo.moead import MOEAD, ParallelMOEAD
 from pymoo.algorithms.moo.nsga2 import NSGA2
 from pymoo.algorithms.moo.rvea import RVEA
-from pymoo.algorithms.soo.convex.nonderiv.cmaes import CMAES
-from pymoo.algorithms.soo.convex.nonderiv.nelder_mead import NelderMead
-from pymoo.algorithms.soo.convex.nonderiv.pattern_search import PatternSearch
+from pymoo.algorithms.soo.nonconvex.cmaes import CMAES
+from pymoo.algorithms.soo.nonconvex.nelder_mead import NelderMead
+from pymoo.algorithms.soo.nonconvex.pattern_search import PatternSearch
 from pymoo.algorithms.soo.nonconvex.de import DE
 from pymoo.algorithms.soo.nonconvex.direct import DIRECT
 from pymoo.algorithms.soo.nonconvex.pso import PSO
-from pymoo.factory import Sphere, Rosenbrock, Himmelblau, ZDT1, ZDT2, ZDT3, get_reference_directions
+from pymoo.factory import Sphere, Rosenbrock, Himmelblau, ZDT1, get_reference_directions
 from pymoo.optimize import minimize
 import numpy as np
 

tests/algorithms/test_pattern_search.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from pymoo.algorithms.soo.convex.nonderiv.pattern_search import PatternSearch
+from pymoo.algorithms.soo.nonconvex.pattern_search import PatternSearch
 from pymoo.operators.sampling.random_sampling import FloatRandomSampling
 from pymoo.optimize import minimize
 from pymoo.problems.single import Sphere, Himmelblau

tests/algorithms/test_single_objective.py

@@ -1,7 +1,7 @@
 import numpy as np
 
-from pymoo.algorithms.soo.convex.nonderiv.nelder_mead import NelderMead
-from pymoo.algorithms.soo.convex.nonderiv.pattern_search import PatternSearch
+from pymoo.algorithms.soo.nonconvex.nelder_mead import NelderMead
+from pymoo.algorithms.soo.nonconvex.pattern_search import PatternSearch
 from pymoo.algorithms.soo.nonconvex.ga import GA
 from pymoo.algorithms.soo.nonconvex.pso import PSO
 from pymoo.factory import Sphere, Problem