# Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems

arXiv: https://arxiv.org/abs/2203.13447 · HTML: https://mlsanity.com/paper/2203.13447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [An Easy-to-use Real-world Multi-objective Optimization Problem Suite](https://mlsanity.com/paper/2009.12867/) (2020)
- [Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit](https://mlsanity.com/paper/1612.07603/) (2016)
- [Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances](https://mlsanity.com/paper/1908.01720/) (2019)
- [One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes](https://mlsanity.com/paper/2006.11547/) (2020)
- [The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition](https://mlsanity.com/paper/1807.06731/) (2018)
- [MOEA/D with Random Partial Update Strategy](https://mlsanity.com/paper/2001.06980/) (2020)
- [Search Trajectories Networks of Multiobjective Evolutionary Algorithms](https://mlsanity.com/paper/2201.11726/) (2022)
- [Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations](https://mlsanity.com/paper/2011.09722/) (2020)
- [Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition](https://mlsanity.com/paper/2112.11939/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multiobjective Evolutionary Component Effect on Algorithm behavior](https://mlsanity.com/paper/2308.02527/) (2023)

## Abstract (arXiv)

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential in problems where MOEA/D performs the worst.