Solving　dynamic　multiobjective　optimization　problems　(DMOPs)　is　extremely　challenging　due　to　the　need　to　address　multiple　conflicting　objectives　that　change　over　time.　Transfer　prediction-　based　strategies　typically　leverage　solutions　from　historical　environments　to　generate　an　initial　population　for　a　new　environment.　However,　these　strategies　often　overlook　the　similarity　between　the　historical　and　new　environments,　which　can　negatively　impact　the　quality　of　the　initial　population.　To　address　this　issue,　we　propose　a　similar　environment　transfer　strategy.　Firstly,　we　select　Pareto-optimal　solutions　from　a　randomly　generated　population　in　the　new　environment　to　form　a　prior　Pareto　set　(PS).　The　prior　PS　is　expand　by　oversampling　sparse　solutions.　Then,　we　apply　the　maximum　mean　discrepancy　(MMD)　to　measure　the　discrepancy　between　the　prior　PS　and　the　PS　from　each　historical　environment.　The　historical　environment　with　the　smallest　MMD　is　identified　as　the　similar　environment.　Finally,　we　use　solutions　from　this　similar　environment　to　establish　a　kernelized　easy　transfer　learning　model,　which　is　employed　to　predict　the　quality　of　random　solutions　in　the　new　environment.　The　initial　population　is　formed　by　combining　excellent　solutions　predicted　by　the　model　with　the　prior　PS.　Experimental　results　demonstrate　that　the　proposed　strategy　significantly　outperforms　several　state-of-the-art　strategies.