# Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition

arXiv: https://arxiv.org/abs/2112.11939 · HTML: https://mlsanity.com/paper/2112.11939/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites](https://mlsanity.com/paper/1604.00359/) (2016)
- [The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition](https://mlsanity.com/paper/1807.06731/) (2018)
- [On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D](https://mlsanity.com/paper/2004.06961/) (2020)
- [MOEA/D with Random Partial Update Strategy](https://mlsanity.com/paper/2001.06980/) (2020)

## Built on by (most cited first, 2 of 2)

- [Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems](https://mlsanity.com/paper/2203.13447/) (2022)
- [Multiobjective Evolutionary Component Effect on Algorithm behavior](https://mlsanity.com/paper/2308.02527/) (2023)

## Abstract (arXiv)

The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA. This study investigates the effects of MOEA/D with the Partial Update Strategy in an extensive set of MOPs to generate insights into correspondences of MOEA/D with the Partial Update and MOEA/D with small population size and big population size. Our work undertakes an in-depth analysis of the populational dynamics behaviour considering their final approximation Pareto sets, anytime hypervolume performance, attained regions and number of unique non-dominated solutions. Our results indicate that MOEA/D with Partial Update progresses with the search as fast as MOEA/D with small population size and explores the search space as MOEA/D with big population size. MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicates.