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https://github.com/thieu1995/enoppy
ENOPPY: A Python Library for Engineering Optimization Problems
https://github.com/thieu1995/enoppy
benchmark-problems chemical-process-problems constrained-problems engineering-optimization engineering-problems livestock-feed-ration-optimization mathematical-optimization mechanical-design-problems multi-objectives-optimization-problems power-system-problems process-design-and-synthesis-problems real-world-optimization rolling-element-bearing-design-problems
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ENOPPY: A Python Library for Engineering Optimization Problems
- Host: GitHub
- URL: https://github.com/thieu1995/enoppy
- Owner: thieu1995
- License: gpl-3.0
- Created: 2023-05-20T04:03:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-18T22:14:02.000Z (about 1 year ago)
- Last Synced: 2024-09-27T04:49:58.469Z (3 months ago)
- Topics: benchmark-problems, chemical-process-problems, constrained-problems, engineering-optimization, engineering-problems, livestock-feed-ration-optimization, mathematical-optimization, mechanical-design-problems, multi-objectives-optimization-problems, power-system-problems, process-design-and-synthesis-problems, real-world-optimization, rolling-element-bearing-design-problems
- Language: Python
- Homepage: https://enoppy.readthedocs.io
- Size: 154 KB
- Stars: 10
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README
---
[](https://github.com/thieu1995/enoppy/releases)
[](https://pypi.python.org/pypi/enoppy)
[](https://badge.fury.io/py/enoppy)
[](https://pepy.tech/project/enoppy)
[](https://github.com/thieu1995/enoppy/actions/workflows/publish-package.yaml)
[](https://enoppy.readthedocs.io/en/latest/?badge=latest)
[](https://t.me/+fRVCJGuGJg1mNDg1)
[](http://isitmaintained.com/project/thieu1995/enoppy "Average time to resolve an issue")
[](http://isitmaintained.com/project/thieu1995/enoppy "Percentage of issues still open")
[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)
[](https://doi.org/10.5281/zenodo.7953206)
[](https://www.gnu.org/licenses/gpl-3.0)
ENOPPY (ENgineering Optimization Problems in PYthon) is the largest python library for real-world engineering
optimization problems. Contains all real-world engineering problems from CEC competitions and research papers.
* **Free software:** GNU General Public License (GPL) V3 license
* **Total problems**: > 50 problems
* **Documentation:** https://enoppy.readthedocs.io/en/latest/
* **Python versions:** 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x
* **Dependencies:** numpy, scipy
# Installation
Install the [current PyPI release](https://pypi.python.org/pypi/enoppy):
```sh
$ pip install enoppy
```
After installation, you can import ENOPPY as any other Python module:
```sh
$ python
>>> import enoppy
>>> enoppy.__version__
```
# Usage
This is a minimal usage example of the enoppy library.
1) How to get the problem and use it
```python
from enoppy.paper_based.moeosma_2023 import SpeedReducerProblem
# SRP = SpeedReducerProblem
# SP = SpringProblem
# HTBP = HydrostaticThrustBearingProblem
# VPP = VibratingPlatformProblem
# CSP = CarSideImpactProblem
# WRMP = WaterResourceManagementProblem
# BCP = BulkCarriersProblem
# MPBPP = MultiProductBatchPlantProblem
srp_prob = SpeedReducerProblem()
print("Lower bound for this problem: ", srp_prob.lb)
print("Upper bound for this problem: ", srp_prob.ub)
x0 = srp_prob.create_solution()
print("Get the objective values of x0: ", srp_prob.get_objs(x0))
print("Get the constraint values of x0: ", srp_prob.get_cons(x0))
print("Evaluate with default penalty function: ", srp_prob.evaluate(x0))
```
2) Design my own penalty function:
```python
import numpy as np
from enoppy.paper_based.moeosma_2023 import HTBP
# HTBP = HydrostaticThrustBearingProblem
def penalty_func(list_objectives, list_constraints):
list_constraints[list_constraints < 0] = 0
return np.sum(list_objectives) + 1e5 * np.sum(list_constraints**2)
htbp_prob = HTBP(f_penalty=penalty_func)
print("Lower bound for this problem: ", htbp_prob.lb)
print("Upper bound for this problem: ", htbp_prob.ub)
x0 = htbp_prob.create_solution()
print("Get the objective values of x0: ", htbp_prob.get_objs(x0))
print("Get the constraint values of x0: ", htbp_prob.get_cons(x0))
print("Evaluate with default penalty function: ", htbp_prob.evaluate(x0))
```
For more examples, check out [examples](/examples) folder and the [enoppy](https://enoppy.readthedocs.io/) documentation
# Get helps (questions, problems)
* Official source code repo: https://github.com/thieu1995/enoppy
* Official document: https://enoppy.readthedocs.io/
* Download releases: https://pypi.org/project/enoppy/
* Issue tracker: https://github.com/thieu1995/enoppy/issues
* Notable changes log: https://github.com/thieu1995/enoppy/blob/master/ChangeLog.md
* Examples with different meapy version: https://github.com/thieu1995/enoppy/blob/master/examples.md
* Join our telegram community: [link](https://t.me/+fRVCJGuGJg1mNDg1)
* This project also related to our another projects which are "meta-heuristics", "neural-network", and "optimization"
check it here
* https://github.com/thieu1995/mealpy
* https://github.com/thieu1995/permetrics
* https://github.com/thieu1995/opfunu
* https://github.com/thieu1995/metaheuristics
* https://github.com/thieu1995/MetaCluster
* https://github.com/thieu1995/pfevaluator
* https://github.com/thieu1995/IntelELM
* https://github.com/thieu1995/MetaPerceptron
* https://github.com/thieu1995/GrafoRVFL
* https://github.com/thieu1995/reflame
* https://github.com/aiir-team
# Acknowledgments
If you are using enoppy in your project, we would appreciate citations:
```code
@software{nguyen_van_thieu_2023_7953207,
author = {Nguyen Van Thieu},
title = {ENOPPY: A Python Library for Engineering Optimization Problems},
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.7953206},
url = {https://github.com/thieu1995/enoppy}
}
@article{van2023mealpy,
title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
author={Van Thieu, Nguyen and Mirjalili, Seyedali},
journal={Journal of Systems Architecture},
year={2023},
publisher={Elsevier},
doi={10.1016/j.sysarc.2023.102871}
}
```
## References
#### paper_based
* **ihaoavoa_2022**: Xiao, Y., Guo, Y., Cui, H., Wang, Y., Li, J., & Zhang, Y. (2022). IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems. Mathematical Biosciences and Engineering, 19(11), 10963-11017.
* **moeosma_2023**: Luo, Q., Yin, S., Zhou, G., Meng, W., Zhao, Y., & Zhou, Y. (2023). Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems. Structural and Multidisciplinary Optimization, 66(5), 114.
* **pdo_2022**: Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 34(22), 20017-20065.
* **rwco_2020**: Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., & Das, S. (2020). A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation, 56, 100693.