This　paper　proposes　a　new　prediction-based　dynamic　multiobjective　optimization　(PBDMO)　method,　which　combines　a　new　prediction-based　reaction　mechanism　and　a　popular　regularity　model-based　multiobjective　estimation　of　distribution　algorithm　(RM-MEDA)　for　solving　dynamic　multiobjective　optimization　problems.　Whenever　a　change　is　detected,　PBDMO　reacts　effectively　to　it　by　generating　three　subpopulations　based　on　different　strategies.　The　first　subpopulation　is　created　by　moving　nondominated　individuals　using　a　simple　linear　prediction　model　with　different　step　sizes.　The　second　subpopulation　consists　of　some　individuals　generated　by　a　novel　sampling　strategy　to　improve　population　convergence　as　well　as　distribution.　The　third　subpopulation　comprises　some　individuals　generated　using　a　shrinking　strategy　based　on　the　probability　distribution　of　variables.　These　subpopulations　are　tailored　to　form　a　population　for　the　new　environment.　The　experimental　results　carried　out　on　a　variety　of　bi-　and　three-objective　benchmark　functions　demonstrate　that　the　proposed　technique　has　competitive　performance　compared　with　some　state-of-the-art　algorithms.