  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.
