Dynamic multiobjective optimization problems require an algorithm to continuously track a changing Pareto
optimal solutions over time. Therefore, a new predictive multiobjective genetic algorithm(PMGA) is proposed, in which
the centroid of Pareto optimal is soluted by clustering. And Pareto optimal solutions are described by applying the centroid
points and reference solutions. Then the prediction set is generated by using the inertia predict and Gauss mutation. After
an environment changed, the prediction set is incorporated in the current population to increase the population diversity
by guided fashion. Finally, experimental studies on dynamic multiobjective optimization problems are carried out. The
simulation results show that PMGA can quickly adapt the dynamic environments and track Pareto optimal solutions.