pymoode: Differential Evolution in Python

A Python framework for Differential Evolution using pymoo [1].

Install

First, make sure you have a Python 3 environment installed.

From PyPi:

pip install pymoode

From the current version on github:

pip install -e git+https://github.com/mooscalia/pymoode#egg=pymoode

New features

This package was written as an extension of pymoo, providing some additional features for DE algorithms and survival operators. One might refer to the sections Algorithms, Survival and Rank and Crowding for more details.

For instance, these solutions for the DTLZ2 problem were obtained using GDE3 with the M-Nearest Neighbors crowding metric.

Algorithms

• Algorithms
  • DE
  • GDE3
  • NSDE
  • NSDE-R

Usage

• Complete tutorial
• Single-objective
• Multi-objective
• Many-objective
• Portfolio example

Theory

• Differential Evolution
• Survival
• Rank and Crowding

Package

• pymoode
  • Algorithms
    • pymoode.algorithms
    • pymoode.algorithms.base
  • Survival
    • pymoode.survival
  • Indicators
    • pymoode.performance
  • Operators
    • pymoode.operators.des
    • pymoode.operators.dex

Citation

This package was developed as part of an academic optimization project [2], as well as pymoo [1]. Please, if you use it for research purposes, cite it accordingly:

Blank, J. & Deb, K., 2020. pymoo: Multi-Objective Optimization in Python. IEEE Access, Volume 8, pp. 89497-89509. doi:10.1109/ACCESS.2020.2990567.

Leite, B., Costa, A. O. S., Costa, E. F., 2023. Multi-objective optimization of adiabatic styrene reactors using Generalized Differential Evolution 3 (GDE3). Chem. Eng. Sci., Volume 265, Article 118196. doi:10.1016/j.ces.2022.118196.

References

To the complete reference list, please refer to this page.