  Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization
problems that objective functions will change with time. Solving DMOPs implies
that the Pareto Optimal Set (POS) at different moments can be accurately found,
and this is a very difficult job due to the dynamics of the optimization
problems. The POS that have been obtained in the past can help us to find the
POS of the next time more quickly and accurately. Therefore, in this paper we
present a Support Vector Machine (SVM) based Dynamic Multi-Objective
Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the
POS that has been obtained to train a SVM and then take the trained SVM to
classify the solutions of the dynamic optimization problem at the next moment,
and thus it is able to generate an initial population which consists of
different individuals recognized by the trained SVM. The initial populuation
can be fed into any population based optimization algorithm, e.g., the
Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that
moment. The experimental results show the validity of our proposed approach.
