  Data-driven evolutionary multi-objective optimization (EMO) has been
recognized as an effective approach for multi-objective optimization problems
with expensive objective functions. The current research is mainly developed
for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas
the performance can significantly deteriorate when the PF consists of
disconnected segments. Furthermore, the offspring reproduction in the current
data-driven EMO does not fully leverage the latent information of the surrogate
model. Bearing these considerations in mind, this paper proposes a data-driven
EMO algorithm based on multiple-gradient descent. By leveraging the regularity
information provided by the up-to-date surrogate model, it is able to
progressively probe a set of well distributed candidate solutions with a
convergence guarantee. In addition, its infill criterion recommends a batch of
promising candidate solutions to conduct expensive objective function
evaluations. Experiments on $33$ benchmark test problem instances with
disconnected PFs fully demonstrate the effectiveness of our proposed method
against four selected peer algorithms.
