A multi-objective swarm optimizer based on SMPSO that uses CDAS as the primary discriminator instead of Pareto dominance and a secondary selection metric based on shift-based density estimators

Implementation of the MOEA Entropy based automatic termination algorithm (Saxena et al. 2016)

An optimization framework for multi-objective evolutionary algorithms

Distributed Multi-Objective Evolutionary Computation Framework for Spark

Multi-Objective Evolutionary Algorithms implemented in .NET

Comparison of MOEAs with statistical methods.

Java implementation of MOEAFramework

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

Proyecto de Titulo Universidad del Bio-Bio

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

an implementation of NSGA-II in java

Genetic Algorithms for Feature Selection, Solving a variant of the Multi-Depot Vehicle Routing Problem (MDVRP) using a Genetic Algorithm (GA), and Image Segmentation With a Multiobjective Evolutionary Algorithm

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

This GOMORS algorithm is the modified version of what is uploaded in this repository: https://github.com/drkupi/GOMORS_pySOT.

Test Functions for Multi-Objective Optimization

The relevant codes of our work "Enhancing Robustness and Transmission Performance of Heterogeneous Complex Networks via Multi-Objective Optimization".

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.

MOEA/D with distribution control of weight vector set

MOEA/D with virtual objective vectors

MOEA/D with Pareto front estimation