  Existing work on data-driven optimization focuses on problems in static
environments, but little attention has been paid to problems in dynamic
environments. This paper proposes a data-driven optimization algorithm to deal
with the challenges presented by the dynamic environments. First, a data stream
ensemble learning method is adopted to train the surrogates so that each base
learner of the ensemble learns the time-varying objective function in the
previous environments. After that, a multi-task evolutionary algorithm is
employed to simultaneously optimize the problems in the past environments
assisted by the ensemble surrogate. This way, the optimization tasks in the
previous environments can be used to accelerate the tracking of the optimum in
the current environment. Since the real fitness function is not available for
verifying the surrogates in offline data-driven optimization, a support vector
domain description that was designed for outlier detection is introduced to
select a reliable solution. Empirical results on six dynamic optimization
benchmark problems demonstrate the effectiveness of the proposed algorithm
compared with four state-of-the-art data-driven optimization algorithms.
