  Dynamic multi-objective optimisation (DMO) handles optimisation problems with
multiple (often conflicting) objectives in varying environments. Such problems
pose various challenges to evolutionary algorithms, which have popularly been
used to solve complex optimisation problems, due to their dynamic nature and
resource restrictions in changing environments. This paper proposes vector
autoregressive evolution (VARE) consisting of vector autoregression (VAR) and
environment-aware hypermutation to address environmental changes in DMO. VARE
builds a VAR model that considers mutual relationship between decision
variables to effectively predict the moving solutions in dynamic environments.
Additionally, VARE introduces EAH to address the blindness of existing
hypermutation strategies in increasing population diversity in dynamic
scenarios where predictive approaches are unsuitable. A seamless integration of
VAR and EAH in an environment-adaptive manner makes VARE effective to handle a
wide range of dynamic environments and competitive with several popular DMO
algorithms, as demonstrated in extensive experimental studies. Specially, the
proposed algorithm is computationally 50 times faster than two widely-used
algorithms (i.e., TrDMOEA and MOEA/D-SVR) while producing significantly better
results.
