Search Trajectories Networks of Multiobjective Evolutionary Algorithms

TL;DR

This paper extends search trajectory networks to model and analyze the search behavior of multiobjective evolutionary algorithms, providing insights into their dynamics on benchmark problems.

Contribution

It introduces a network-based approach using STNs to distinguish and understand the search behaviors of MOEA/D and NSGA-II.

Findings

STNs can effectively model MOEA search dynamics

Different algorithms exhibit distinguishable search patterns

The approach enhances understanding of MOEA behavior

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.