Search Trajectories Networks of Multiobjective Evolutionary Algorithms
This work addresses the open problem of analyzing multiobjective evolutionary algorithms for researchers in optimization, but it is incremental as it applies an existing network-based tool to a new context.
The paper tackled the problem of understanding the search dynamics of multiobjective evolutionary algorithms by extending search trajectory networks to model their behavior, showing that this approach can distinguish between MOEA/D and NSGA-II on 10 benchmark problems with 2 and 3 objectives.
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.