  Multi-objective optimization problems are ubiquitous in real-world science,
engineering and design optimization problems. It is not uncommon that the
objective functions are as a black box, the evaluation of which usually involve
time-consuming and/or costly physical experiments. Data-driven evolutionary
optimization can be used to search for a set of non-dominated trade-off
solutions, where the expensive objective functions are approximated as a
surrogate model. In this paper, we propose a framework for implementing batched
data-driven evolutionary multi-objective optimization. It is so general that
any off-the-shelf evolutionary multi-objective optimization algorithms can be
applied in a plug-in manner. In particular, it has two unique components: 1)
based on the Karush-Kuhn-Tucker conditions, a manifold interpolation approach
that explores more diversified solutions with a convergence guarantee along the
manifold of the approximated Pareto-optimal set; and 2) a batch recommendation
approach that reduces the computational time of the optimization process by
evaluating multiple samples at a time in parallel. Experiments on 136 benchmark
test problem instances with irregular Pareto-optimal front shapes against six
state-of-the-art surrogate-assisted EMO algorithms fully demonstrate the
effectiveness and superiority of our proposed framework. In particular, our
proposed framework is featured with a faster convergence and a stronger
resilience to various PF shapes.
