The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition

by   Felipe Campelo, et al.

Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easier reproducibility of existing MOEA/D variants from the literature, as well as for faster development and testing of new composite algorithms. The package offers an standardized, modular implementation of MOEA/D based on this framework, which was designed aiming at providing researchers and practitioners with a standard way to discuss and express MOEA/D variants. In this paper we introduce the design principles behind the MOEADr package, as well as its current components. Three case studies are provided to illustrate the main aspects of the package.



There are no comments yet.


page 1

page 2

page 3

page 4


copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas

The use of copula-based models in EDAs (estimation of distribution algor...

A synthetic biology approach for the design of genetic algorithms with bacterial agents

Bacteria have been a source of inspiration for the design of evolutionar...

Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms

In this paper we systematically study the importance, i.e., the influenc...

NEP-PACK: A Julia package for nonlinear eigenproblems - v0.2

We present NEP-PACK a novel open-source library for the solution of nonl...

ZpL: a p-adic precision package

We present a new package ZpL for the mathematical software system SM. It...

rnn : Recurrent Library for Torch

The rnn package provides components for implementing a wide range of Rec...

Birth-and-death Processes in Python: The BirDePy Package

Birth-and-death processes (BDPs) form a class of continuous-time Markov ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.