# Search Trajectories Networks of Multiobjective Evolutionary Algorithms

arXiv: https://arxiv.org/abs/2201.11726 · HTML: https://mlsanity.com/paper/2201.11726/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [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)
- [To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes](https://mlsanity.com/paper/2011.14395/) (2020)

## Built on by (most cited first, 3 of 3)

- [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)
- [Semantic Space Search Trajectory Networks](https://mlsanity.com/paper/2607.25122/) (2026)

## Abstract (arXiv)

Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis.