Python package for the Multi-Objective Design of electro-mechanical Actuators that is used to derive 20 benchmark problems for constrained multi-objective optimization.
For more information about the framework, please refer to the associate publication:
C. Picard and J. Schiffmann, “Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2020, doi: 10.1109/TEVC.2020.3020046.
If you use MODAct in your research, we would appreciate a citation.
modact has a few requirements listed in requirements.txt. In particular,
python-fcl needs to be
installed along with the required fcl shared library.
The easiest way to get started is to build a Docker image.
docker build -t modact .Otherwise, users can install fcl through their package manager (apt, brew, vcpkg) and then run:
pip install -r requirements.txt
python setup.py installEach benchmark problem is in a self-contained object:
import modact.problems as pb
# Create problem
cs1 = pb.get_problem('cs1')
# Get search bounds
xl, xu = cs1.bounds()
# Objective weights: -1 --> minimization / 1 --> maximization
cs1.weights # (-1, 1)
# Constraints weights: -1 --> g(x) >= 0 / 1 --> g(x) <= 0
cs1.c_weights # (-1, -1, -1, -1, -1, -1, -1)
# To evaluate a vector
f, g = cs1(xl)Note that the output of the function call is not per se automatically converted
to a minimization problem. The weights and c_weights tuples need to be used.
An example of how this is done is given in the adapter for pymoo:
modact.interfaces.pymoo.
Usage examples are shown in the scripts folder. In particular, optimization
example using pymoo are given.
Interfaces form different languages (C++ and MATLAB) to python are provided in
the interfaces folder.
The best-known Pareto fronts approximations of the 20 problems can be downloaded
here:
