Installation

To install DetPy, use the following command:

pip install detpy

You can find the full source code on GitHub: DetPy Repository.

Introduction

DetPy (Differential Evolution Tools) is a library designed to help scientists and engineers solve complex optimization problems using the differential evolution algorithm along with numerous variants.

Key Features

• Implementations of popular and state-of-the-art differential evolution methods.

• Flexibility to configure algorithm parameters.

• Visualization tools to monitor results.

• Support for benchmarking against standard optimization functions.

• Option to store results in an SQLite database.

User Guide

Purpose

The goal of DetPy is to simplify the application of differential evolution algorithms for researchers and practitioners in the field of optimization.

Getting Started

To begin using DetPy, follow these steps:

1. Installation As mentioned above, install the library using pip.

2. Import the Library

   import detpy
   

3. Basic Usage

   Here’s a quick example of using DetPy to solve a simple optimization problem:

   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()
   

Examples

To make the library more accessible, the documentation includes various examples covering:

• Optimization of the Ackley function based on SADE.

• Optimization of the Ackley function with all DE variants.

• Optimization of common benchmark functions.

• Optimization of functions from Opfunu.

Check out the Examples Section for more details.