Quick Start¶

Installation¶

Install the current PyPI release:
```
$ pip install enoppy==0.1.1
```

Install directly from source code:

$ git clone https://github.com/thieu1995/enoppy.git
$ cd enoppy
$ python setup.py install

Lib’s structure¶

Current’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](/examples) folder.