  Decomposition-based multiobjective evolutionary algorithms (MOEAs) with
clustering-based reference vector adaptation show good optimization performance
for many-objective optimization problems (MaOPs). Especially, algorithms that
employ a clustering algorithm with a topological structure (i.e., a network
composed of nodes and edges) show superior optimization performance to other
MOEAs for MaOPs with irregular Pareto optimal fronts (PFs). These algorithms,
however, do not effectively utilize information of the topological structure in
the search process. Moreover, the clustering algorithms typically used in
conventional studies have limited clustering performance, inhibiting the
ability to extract useful information for the search process. This paper
proposes an adaptive reference vector-guided evolutionary algorithm using an
adaptive resonance theory-based clustering with a topological structure. The
proposed algorithm utilizes the information of the topological structure not
only for reference vector adaptation but also for mating selection. The
proposed algorithm is compared with 8 state-of-the-art MOEAs on 78 test
problems. Experimental results reveal the outstanding optimization performance
of the proposed algorithm over the others on MaOPs with various properties.
