Conference Paper (published)
Details
Citation
Lavinas Y, Aranha C & Ochoa G (2022) Search Trajectories Networks of Multiobjective Evolutionary Algorithms. In: Jiménez Laredo JL, Hidalgo JI & Babaagba KO (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, 13224. EvoApplications 2022, Madrid, Spain, 20.04.2022-22.04.2022. Cham, Switzerland: Springer International Publishing, pp. 223-238. https://doi.org/10.1007/978-3-031-02462-7_15
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.
Keywords
Algorithm analysis; Search trajectories; Continuous optimization; Visualization; Multi-objective optimization
| Status | Published |
|---|---|
| Title of series | Lecture Notes in Computer Science |
| Number in series | 13224 |
| Publication date | 31/12/2022 |
| Publication date online | 15/04/2022 |
| URL | |
| Publisher | Springer International Publishing |
| Place of publication | Cham, Switzerland |
| ISSN of series | 0302-9743 |
| ISBN | 9783031024610 |
| eISBN | 9783031024627 |
| Conference | EvoApplications 2022 |
| Conference location | Madrid, Spain |
| Dates | – |
People (1)
Professor, Computing Science