Project-Platypus/Platypus

A Free and Open Source Python Library for Multiobjective Optimization

What it solves

Platypus provides a framework for evolutionary computing, specifically addressing the challenge of multiobjective optimization—where multiple, often conflicting goals must be optimized simultaneously.

How it works

It implements a variety of multiobjective evolutionary algorithms (MOEAs) in Python, allowing users to define a problem with specific decision variables and objective functions, then run an algorithm to find the optimal set of solutions.

Who it’s for

Researchers and developers who need to perform multiobjective optimization using evolutionary computing techniques in Python.

Highlights

  • Supports a wide range of algorithms including NSGA-II, NSGA-III, MOEA/D, IBEA, Epsilon-MOEA, SPEA2, GDE3, OMOPSO, SMPSO, and Epsilon-NSGA-II.
  • Provides analysis tools specifically designed for multiobjective optimization.
  • Simple API for defining problems and running optimization tasks.

Related

  • Project
    BIMK/PlatEMO

    PlatEMO is a MATLAB platform for evolutionary multi-objective optimization, offering 300+ algorithms and 600+ benchmark problems with a user-friendly GUI and parallel experiment support. It helps researchers test and compare optimization methods easily.

  • Project
    SciML/Evolutionary.jl

    A Julia package providing a comprehensive suite of of evolutionary and genetic algorithms for complex optimization problems.

  • Project
    OpenMDAO/OpenMDAO

    A high-performance computing platform for systems analysis and multidisciplinary optimization that enables the decomposition and parallel solving of complex models.

  • Project
    skydiscover-ai/skydiscover

    A framework for AI-driven scientific discovery and system synthesis that uses evolutionary algorithms and multi-agent systems to optimize algorithms and build high-performance software.

  • Project
    Valdecy/pyMetaheuristic

    A Python library providing over 400 metaheuristic optimization algorithms with support for cooperative multi-island search, constrained optimization, and scientific benchmarking tools.