  The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs)
is that optimization objective functions will change with times or
environments. One of the promising approaches for solving the DMOPs is reusing
the obtained Pareto optimal set (POS) to train prediction models via machine
learning approaches. In this paper, we train an Incremental Support Vector
Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP
we want to solve at the next moment are filtered through the trained ISVM
classifier. A high-quality initial population will be generated by the ISVM
classifier, and a variety of different types of population-based dynamic
multi-objective optimization algorithms can benefit from the population. To
verify this idea, we incorporate the proposed approach into three evolutionary
algorithms, the multi-objective particle swarm optimization(MOPSO),
Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity
Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We
employ experiments to test these algorithms, and experimental results show the
effectiveness.
