enoppy 0.1.1

ENOPPY: A Python Library for Engineering Optimization Problems

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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, 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

Want to have an instant assistant? Join our telegram community at link We share lots of information, questions, and answers there. You will get more support and knowledge there.

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.