  Dynamic environments pose great challenges for expensive optimization
problems, as the objective functions of these problems change over time and
thus require remarkable computational resources to track the optimal solutions.
Although data-driven evolutionary optimization and Bayesian optimization (BO)
approaches have shown promise in solving expensive optimization problems in
static environments, the attempts to develop such approaches in dynamic
environments remain rarely unexplored. In this paper, we propose a simple yet
effective meta-learning-based optimization framework for solving expensive
dynamic optimization problems. This framework is flexible, allowing any
off-the-shelf continuously differentiable surrogate model to be used in a
plug-in manner, either in data-driven evolutionary optimization or BO
approaches. In particular, the framework consists of two unique components: 1)
the meta-learning component, in which a gradient-based meta-learning approach
is adopted to learn experience (effective model parameters) across different
dynamics along the optimization process. 2) the adaptation component, where the
learned experience (model parameters) is used as the initial parameters for
fast adaptation in the dynamic environment based on few shot samples. By doing
so, the optimization process is able to quickly initiate the search in a new
environment within a strictly restricted computational budget. Experiments
demonstrate the effectiveness of the proposed algorithm framework compared to
several state-of-the-art algorithms on common benchmark test problems under
different dynamic characteristics.
