mdfh-scp 1.0.0

A Multidimensional Fitness Heuristic solver for Set Covering Problems.

mdfh-scp

A Multidimensional Fitness Heuristic solver for Set Covering Problems.

Overview

mdfh-scp is a lightweight Python implementation of a Multidimensional Fitness Heuristic for Set Covering Problems.

The implementation is inspired by the heuristic algorithm presented in:

Hashemi et al. (2025), "A multidimensional fitness function based heuristic algorithm for set covering problems", Applied Soft Computing.

Installation

pip install mdfh-scp

Development installation

git clone https://github.com/gsadra/mdfh-scp.git
cd mdfh-scp
pip install -e .[dev]

Usage

from mdfh import MDFHSetCoverSolver

variables = ["x1", "x2", "x3"]

fitness_cost_component = {
"x1": 10.0,
"x2": 8.0,
"x3": 6.0,
}

fitness_time_component = {
"x1": 1.0,
"x2": 1.0,
"x3": 1.0,
}

fitness_route_component = {
"x1": 1.0,
"x2": 1.0,
"x3": 1.0,
}

coverage_weight = {
"x1": {"x2"},
"x2": {"x1", "x3"},
"x3": {"x3"},
}

solver = MDFHSetCoverSolver(
variables=variables,
fitness_cost_component=fitness_cost_component,
fitness_time_component=fitness_time_component,
fitness_route_component=fitness_route_component,
coverage_weight=coverage_weight,
include_self=True,
)

result = solver.solve(verbose=True)

print(result.selected_variables)
print(result.objective_value)
print(result.is_feasible)

API

MDFHSetCoverSolver

MDFHSetCoverSolver(
variables,
fitness_cost_component,
fitness_time_component,
fitness_route_component,
coverage_weight,
include_self=True,
)

Parameters

• variables: List of variable identifiers.
• fitness_cost_component: Cost-related fitness component.
• fitness_time_component: Time-related fitness component.
• fitness_route_component: Route-related fitness component.
• coverage_weight: Mapping of each variable to the variables it covers.
• include_self: If True, each variable covers itself.

solve(verbose=False)

Runs the MDFH heuristic and returns an MDFHResult.

Result Object

The solver returns:

MDFHResult(
selected_variables,
covered_variables,
uncovered_variables,
is_feasible,
objective_value,
solve_duration,
)

Exceptions

• MDFHValidationError: Raised for invalid input data.
• MDFHSolverError: Raised when the solver cannot continue.
• MDFHBaseException: Base class for MDFH exceptions.

Citation

@article{hashemi2025multidimensional,
  title = {A multidimensional fitness function based heuristic algorithm for set covering problems},
  author = {Hashemi, A. and Gholami, H. and Delorme, X. and Wong, K. Y.},
  journal = {Applied Soft Computing},
  volume = {174},
  pages = {113038},
  year = {2025},
  issn = {1568-4946},
  doi = {10.1016/j.asoc.2025.113038}
}

Disclaimer

This is an independent implementation and is not an official implementation provided by the authors of the referenced paper.

Contributing

Contributions are welcome. If you would like to improve this project, fix bugs, add features, improve documentation, or add tests, please follow the steps below.

Fork the Repository

Click the Fork button at the top-right of the repository page on GitHub.

This creates a copy of the repository under your own GitHub account.

Make Your Changes

You can contribute by:

• Fixing bugs
• Adding new examples
• Improving documentation
• Adding tests
• Improving code structure
• Adding new features
• Improving validation or error handling

Please keep the code clear, readable, and well documented.

Reporting Issues

If you find a bug or have a suggestion, please open an issue on GitHub:

https://github.com/sadraagholami/mdfh-scp/issues

When reporting a bug, please include:

- A clear description of the problem
- Steps to reproduce the issue
- Expected behavior
- Actual behavior
- Python version
- Operating system
- Any relevant error messages

License

MIT License.