Add wrapper to MODAct problems

doc/source/portfolio.html

@@ -105,6 +105,7 @@ <h4>Problems</h4>
                     <b>Constrained:</b>
                     CTP,
                     <a href="problems/constrained/dascmop.html">DASCMOP</a>,
+                    <a href="problems/constrained/modact.html">MODAct</a>,
                     <a href="problems/constrained/mw.html">MW</a>,
                     CDTLZ
                     <br>

doc/source/problems/index.ipynb

@@ -243,7 +243,8 @@
     "|[MW13](constrained/mw.ipynb)|15|2|2||\n",
     "|[MW14](constrained/mw.ipynb)|15|3|1||\n",
     "|[SymPart](multi/sym_part.ipynb)|2|2| ||\n",
-    "|[OmniTest](multi/omni_test.ipynb)|s|2| ||"
+    "|[OmniTest](multi/omni_test.ipynb)|s|2| ||\n",
+    "|[MODAct](constrained/modact.ipynb)|20|2-5|7-10|Real-world mechanical design problems|"
    ]
   },
   {
@@ -525,4 +526,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

doc/source/references.bib

@@ -588,3 +588,14 @@ @inproceedings{sym_part
 }
 
 
+
+@article{modact,
+  title = {Realistic {{Constrained Multi}}-{{Objective Optimization Benchmark Problems}} from {{Design}}},
+  author = {Picard, Cyril and Schiffmann, J{\"u}rg},
+  year = {2020},
+  pages = {1--1},
+  issn = {1941-0026},
+  doi = {10.1109/TEVC.2020.3020046},
+  journal = {IEEE Transactions on Evolutionary Computation},
+}
+

pymoo/factory.py

@@ -260,6 +260,7 @@ def get_problem_options():
         ('dascmop7', DASCMOP7),
         ('dascmop8', DASCMOP8),
         ('dascmop9', DASCMOP9),
+        ('modact', MODAct),
         ('mw1', MW1),
         ('mw2', MW2),
         ('mw3', MW3),

pymoo/problems/multi/__init__.py

@@ -3,6 +3,7 @@
 from pymoo.problems.multi.ctp import *
 from pymoo.problems.multi.dascmop import *
 from pymoo.problems.multi.kursawe import *
+from pymoo.problems.multi.modact import *
 from pymoo.problems.multi.mw import *
 from pymoo.problems.multi.osy import *
 from pymoo.problems.multi.tnk import *

pymoo/problems/multi/modact.py

@@ -0,0 +1,60 @@
+import os
+
+import numpy as np
+
+from pymoo.model.problem import Problem
+from pymoo.problems.util import load_pareto_front_from_file
+
+
+class MODAct(Problem):
+    """Multi-Objective Design of Actuators
+
+    MODAct is a framework for real-world constrained multi-objective optimization.
+    Refer to the python package https://github.com/epfl-lamd/modact from requirements.
+
+    Best-known Pareto fronts must be downloaded from here: https://doi.org/10.5281/zenodo.3824302
+
+    Parameters
+    ----------
+
+    function: str or modact.problems
+        The name of the benchmark problem to use either as a string or the
+        problem object instance. Example values: cs1, cs3, ct2, ct4, cts3
+
+    References:
+    ----------
+    C. Picard and J. Schiffmann, “Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design,”
+    IEEE Transactions on Evolutionary Computation, pp. 1–1, 2020.
+    """
+    def __init__(self, function, **kwargs):
+        import modact.problems as pb
+
+        if isinstance(function, pb.Problem):
+            self.fct = function
+        else:
+            self.fct = pb.get_problem(function)
+        lb, ub = self.fct.bounds()
+        n_var = len(lb)
+        n_obj = len(self.fct.weights)
+        n_constr = len(self.fct.c_weights)
+        xl = lb
+        xu = ub
+
+        self.weights = np.array(self.fct.weights)
+        self.c_weights = np.array(self.fct.c_weights)
+
+        super().__init__(n_var=n_var, n_obj=n_obj, n_constr=n_constr, xl=xl,
+                         xu=xu, elementwise_evaluation=True, type_var=np.double,
+                         **kwargs)
+
+    def _evaluate(self, x, out, *args, **kwargs):
+        f, g = self.fct(x)
+        out["F"] = np.array(f)*-1*self.weights
+        out["G"] = np.array(g)*self.c_weights
+
+    def _calc_pareto_front(self, *args, **kwargs):
+        """Loads the corresponding PF if it exists"""
+        fname = f"{self.fct.name}_PF.dat"
+        F = load_pareto_front_from_file(os.path.join("modact", fname))
+        if F is not None:
+            return F*self.weights*-1