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Description:

enoppy 0.1.1

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, matplotlib

Installation
Install with pip
Install the current PyPI release:
$ pip install enoppy==0.1.0

Install directly from source code
$ git clone https://github.com/thieu1995/enoppy.git
$ cd enoppy
$ python setup.py install

Lib's structure
docs
examples
enoppy
paper_based
pdo_2022.py
rwco_2020.py
problem_based
chemical.py
mechanism.py
utils
validator.py
visualize.py
__init__.py
engineer.py
README.md
setup.py

Usage
After installation, you can import ENOPPY as any other Python module:
$ python
>>> import enoppy
>>> enoppy.__version__

Let's go through some examples.
Examples
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))

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 usage examples please look at examples folder.
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

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/metaheuristics
https://github.com/thieu1995/opfunu
https://github.com/thieu1995/permetrics
https://github.com/aiir-team

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Cite Us
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},
month = may,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.7953206},
url = {https://github.com/thieu1995/enoppy}
}

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.