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: 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.paperbased.moeosma2023 import SpeedReducerProblem

SRP = SpeedReducerProblem

SP = SpringProblem

HTBP = HydrostaticThrustBearingProblem

VPP = VibratingPlatformProblem

CSP = CarSideImpactProblem

WRMP = WaterResourceManagementProblem

BCP = BulkCarriersProblem

MPBPP = MultiProductBatchPlantProblem

srpprob = SpeedReducerProblem() print("Lower bound for this problem: ", srpprob.lb) print("Upper bound for this problem: ", srpprob.ub) x0 = srpprob.createsolution() print("Get the objective values of x0: ", srpprob.getobjs(x0)) print("Get the constraint values of x0: ", srpprob.getcons(x0)) print("Evaluate with default penalty function: ", srpprob.evaluate(x0))

```

2) Design my own penalty function:

```python import numpy as np from enoppy.paperbased.moeosma2023 import HTBP

HTBP = HydrostaticThrustBearingProblem

def penaltyfunc(listobjectives, listconstraints): listconstraints[listconstraints < 0] = 0 return np.sum(listobjectives) + 1e5 * np.sum(list_constraints**2)

htbpprob = HTBP(fpenalty=penaltyfunc) print("Lower bound for this problem: ", htbpprob.lb) print("Upper bound for this problem: ", htbpprob.ub) x0 = htbpprob.createsolution() print("Get the objective values of x0: ", htbpprob.getobjs(x0)) print("Get the constraint values of x0: ", htbpprob.getcons(x0)) print("Evaluate with default penalty function: ", htbpprob.evaluate(x0)) ```

For more examples, check out examples folder and the enoppy 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

• 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{nguyenvanthieu20237953207, 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.