# Multi-node environment strategy for Parallel Deterministic Multi-Objective Fractal Decomposition

## TL;DR

This paper introduces a multi-node environment implementation of the deterministic Multiobjective Fractal Decomposition Algorithm (Mo-FDA), enhancing large-scale multi-objective optimization through container-based distributed computing and benchmarking against existing algorithms.

## Contribution

It extends Mo-FDA to multi-node environments using containers, enabling scalable parallel deterministic multi-objective optimization.

## Key findings

- Mo-FDA performs competitively on benchmark problems.
- Container-based deployment improves scalability.
- The approach effectively handles large-scale multi-objective problems.

## Abstract

This paper presents a new implementation of deterministic multiobjective (MO) optimization called Multiobjective Fractal Decomposition Algorithm (Mo-FDA). The original algorithm was designed for mono-objective large scale continuous optimization problems. It is based on a divide and conquer strategy and a geometric fractal decomposition of the search space using hyperspheres. Then, to deal with MO problems a scalarization approach is used. In this work, a new approach has been developed on a multi-node environment using containers. The performance of Mo-FDA was compared to state of the art algorithms from the literature on classical benchmark of multi-objective optimization