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 install

Each 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: