DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution

📖 Citation

If you use DetPy in your research, please cite:

Zieliński, B., Ściegienny, S., Orlicki, H., & Książek, W. (2025).
DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution.
SoftwareX, 29, 102014.
https://doi.org/10.1016/j.softx.2024.102014

BibTeX

@article{Zielinski2025DetPy,
  title   = {DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution},
  author  = {Zieliński, Błażej and Ściegienny, Szymon and Orlicki, Hubert and Książek, Wojciech},
  journal = {SoftwareX},
  volume  = {29},
  pages   = {102014},
  year    = {2025},
  doi     = {10.1016/j.softx.2024.102014}
}

Introduction

The DetPy library contains implementations of the differential evolution algorithm and 18 modifications of this algorithm. It can be used to solve advanced optimization problems. The following variants have been implemented:

Installation

pip install detpy

Example - optimization of the Ackley function based SADE

from detpy.DETAlgs.data.alg_data import SADEData

from detpy.DETAlgs.sade import SADE

from detpy.functions import FunctionLoader

from detpy.models.enums.boundary_constrain import BoundaryFixing

from detpy.models.enums.optimization import OptimizationType

from detpy.models.fitness_function import BenchmarkFitnessFunction


function_loader = FunctionLoader()

ackley_function = function_loader.get_function(function_name="ackley", n_dimensions=2)

fitness_fun = BenchmarkFitnessFunction(ackley_function)


params = SADEData(

    epoch=100,

    population_size=100,

    dimension=2,

    lb=[-32.768, -32.768],

    ub=[32.768, 32.768],

    mode=OptimizationType.MINIMIZATION,

    boundary_constraints_fun=BoundaryFixing.RANDOM,

    function=fitness_fun,

    log_population=True,

    parallel_processing=['thread', 4]

)


default2 = SADE(params, db_conn="Differential_evolution.db", db_auto_write=False)

results = default2.run()

Using FunctionLoader

You can also use one of predefined functions to solve your problem. To do this, call the FunctionLoader method and pass as an argument the name of a function from the folder and variables, which u want to use in your calculations.

function_loader = FunctionLoader()
function_name = "ackley"
variables = [0.0, 0.0]
n_dimensions = 2

result = function_loader.evaluate_function(function_name, variables, n_dimensions)

Available functions:

        self.function_classes = {
            "ackley": Ackley,
            "rastrigin": Rastrigin,
            "rosenbrock": Rosenbrock,
            "sphere": Sphere,
            "griewank": Griewank,
            "schwefel": Schwefel,
            "michalewicz": Michalewicz,
            "easom": Easom,
            "himmelblau": Himmelblau,
            "keane": Keane,
            "rana": Rana,
            "pits_and_holes": PitsAndHoles,
            "hypersphere": Hypersphere,
            "hyperellipsoid": Hyperellipsoid,
            "eggholder": EggHolder,
            "styblinski_tang": StyblinskiTang,
            "goldstein_and_price": GoldsteinAndPrice
        }

Test functions prepared based on https://gitlab.com/luca.baronti/python_benchmark_functions

References

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Documentation

Full documentation is available: https://blazej-zielinski.github.io/detpy/