  Real-world multiobjective optimization problems usually involve conflicting
objectives that change over time, which requires the optimization algorithms to
quickly track the Pareto optimal front (POF) when the environment changes. In
recent years, evolutionary algorithms based on prediction models have been
considered promising. However, most existing approaches only make predictions
based on the linear correlation between a finite number of optimal solutions in
two or three previous environments. These incomplete information extraction
strategies may lead to low prediction accuracy in some instances. In this
paper, a novel prediction algorithm based on incremental support vector machine
(ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic
multiobjective optimization problems (DMOPs) as an online learning process,
using the continuously obtained optimal solution to update an incremental
support vector machine without discarding the solution information at earlier
time. ISVM is then used to filter random solutions and generate an initial
population for the next moment. To overcome the obstacle of insufficient
training samples, a synthetic minority oversampling strategy is implemented
before the training of ISVM. The advantage of this approach is that the
nonlinear correlation between solutions can be explored online by ISVM, and the
information contained in all historical optimal solutions can be exploited to a
greater extent. The experimental results and comparison with chosen
state-of-the-art algorithms demonstrate that the proposed algorithm can
effectively tackle dynamic multiobjective optimization problems.
