Evolutionary algorithm toolbox and framework with high performance for Python

A Python implementation of the decomposition based multi-objective evolutionary algorithm (MOEA/D)

This project is implemented by C#, and introduces a algorithm framework of MOEA, and some MOEA algorithms and multi-objective problems are provided.

MOEA/D is a general-purpose algorithm framework. It decomposes a multi-objective optimization problem into a number of single-objective optimization sub-problems and then uses a search heuristic to optimize these sub-problems simultaneously and cooperatively.

R package MOEADr, a modular implementation of the Multiobjective Evolutionary Algorithm with Decomposition (MOEA/D) framework

An evolutionary many-objective approach to multiview clustering using feature and relational data

Multi-Objective Evolutionary Algorithms implemented in .NET

Bash Script to help statistical tests of Multi-Objective Evolutionary Algorithms.

Official Implementation: A Generalized Scalarization Method for Evolutionary Multi-objective Optimization, AAAI 2023.

Five multi-objective evolutionary algorithms (NSGA-II, MOPSO, SPEA2, PESA-II, MOEA/D) on the MOP2 benchmark problem.

A multi-objective problem of Path Planning based on MOEA/D and NSGA-II

Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) in MATLAB

Official Implementation: Decomposition-Based Multi-Objective Evolutionary Algorithm with Model-Based Ideal Point Estimation, GECCO 2023.

An online version of weight vectors generator for MOEA/D and NSGA-III metaheuristics

A Multiobjective Evolutionary Algorithm Based on Decomposition Implementation

A comprehensive Python implementation of MOEA/D (Multiobjective Evolutionary Algorithm based on Decomposition), a state-of-the-art algorithm for solving multiobjective optimization problems. This implementation is based on the seminal work by Zhang and Li (2007).

(Re)Implementation: Adaptive Replacement Strategies for MOEA/D, TCYB 2016.

Official Implementation: Decomposition with Adaptive Composite Norm for Evolutionary Multi-Objective Combinatorial Optimization, SWEVO 2024.

A unified multi-objective evolutionary optimization architecture benchmarking NSGA-II, NSGA-III, MOEA/D, and SPEA2 using Hypervolume indicators to resolve WSN spatial constraints.

Tese de conclusão de Curso