Communication Dans Un Congrès Année : 2022

Search Trajectories Networks of Multiobjective Evolutionary Algorithms

Résumé

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

Fichier non déposé

Dates et versions

hal-04821816 , version 1 (05-12-2024)

Identifiants

Citer

Yuri Lavinas, Claus Aranha, Gabriela Ochoa. Search Trajectories Networks of Multiobjective Evolutionary Algorithms. Conference, Apr 2022, Madrid, Spain. pp.223-238, ⟨10.1007/978-3-031-02462-7_15⟩. ⟨hal-04821816⟩
26 Consultations
0 Téléchargements

Altmetric

Partager

  • More