Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-02462-7_15 | EvoStar Conferences (EvoStar) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuri Cossich Lavinas | 1 | 1 | 3.40 |
| Claus Aranha | 2 | 0 | 2.37 |
| Gabriela Ochoa | 3 | 76 | 9.48 |