<p align="center"><img src=".github/img/logo.png" alt="ENOPPY" title="ENOPPY"/></p>

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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:

$ pip install enoppy

After installation, you can import ENOPPY as any other Python module:

$ 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

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:

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 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:

@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.