# Multiobjective Evolutionary Component Effect on Algorithm behavior

arXiv: https://arxiv.org/abs/2308.02527 · HTML: https://mlsanity.com/paper/2308.02527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [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)
- [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)
- [An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization](https://mlsanity.com/paper/1409.7478/) (2014)
- [Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems](https://mlsanity.com/paper/2203.13447/) (2022)

## Abstract (arXiv)

The performance of multiobjective evolutionary algorithms (MOEAs) 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 their components. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still unknown what are the most influential components that lead to performance improvements. This study specifies a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a tuned Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) designed by the iterated racing (irace) configuration package on constrained problems of 3 groups: (1) analytical real-world problems, (2) analytical artificial problems and (3) simulated real-world. We then compare the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the anytime hypervolume values. Looking at the objective space behavior, the MOEAs studied converged before half of the search to generally good HV values in the analytical artificial problems and the analytical real-world problems. For the simulated problems, the HV values are still improving at the end of the run. In terms of decision space behavior, we see a diverse set of the trajectories of the STNs in the analytical artificial problems. These trajectories are more similar and frequently reach optimal solutions in the other problems.