Conference Proceeding

Search Trajectories Networks of Multiobjective Evolutionary Algorithms



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.

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.

Algorithm analysis; Search trajectories; Continuous optimization; Visualization; Multi-objective optimization

Title of seriesLecture Notes in Computer Science
Number in series13224
Publication date31/12/2022
Publication date online30/04/2022
PublisherSpringer International Publishing
Place of publicationCham, Switzerland
ISSN of series0302-9743
ConferenceEvoApplications 2022
Conference locationMadrid, Spain

People (1)


Professor Gabriela Ochoa
Professor Gabriela Ochoa

Professor, Computing Science