Merge branch 'main' of https://github.com/anyoptimization/pymoo

docs/source/references.bib

@@ -253,7 +253,7 @@ @article{das_dennis
 }
 
 @book{multi_objective_book,
- author = {Deb, Kalyanmoy and Kalyanmoy, Deb},
+ author = {Kalyanmoy, Deb},
  title = {Multi-Objective Optimization Using Evolutionary Algorithms},
  year = {2001},
  isbn = {047187339X},
@@ -753,4 +753,4 @@ @article{mosade
  pages={193--209},
  year={2010},
  publisher={Springer}
-}
+}

pymoo/core/problem.py

@@ -58,6 +58,23 @@ def __getstate__(self):
  return state
 
 
+class JoblibParallelization:
+
+ def __init__(self, aJoblibParallel, aJoblibDelayed, *args, **kwargs) -> None:
+ super().__init__()
+ self.parallel = aJoblibParallel 
+ self.delayed = aJoblibDelayed 
+
+ def __call__(self, f, X):
+ return self.parallel(self.delayed(f)(x) for x in X)
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ state.pop("parallel", None)
+ state.pop("delayed", None)
+ return state
+
+
 class Problem:
  def __init__(self,
  n_var=-1,

pymoo/gradient/grad_complex.py

@@ -5,7 +5,7 @@
 
 
 def calc_complex_gradient(problem, return_values_of, x, eps, *args, **kwargs):
- xp = x + np.eye(len(x)) * np.complex(0, eps)
+ xp = x + np.eye(len(x)) * complex(0, eps)
  out = problem.do(xp, return_values_of, *args, **kwargs)
 
  grad = {}