This　paper　presents　a　novel　population　prediction　algorithm　based　on　modular　neural　network　(PA-MNN)　for　handling　dynamic　multi-objective　optimization.　The　proposed　algorithm　consists　of　three　mechanisms.　First,　we　set　up　a　modular　neural　network　(MNN)　and　train　it　with　historical　population　information.　Some　of　the　initial　solutions　are　generated　by　the　MNN　when　an　environmental　change　is　detected.　Second,　some　solutions　are　predicted　based　on　forward-looking　center　points.　Finally,　some　solutions　are　generated　randomly　to　maintain　the　diversity.　With　these　mechanisms,　when　the　new　environment　has　been　encountered　before,　initial　solutions　generated　by　MNN　will　have　the　same　distribution　characteristics　as　the　final　solutions　that　were　obtained　in　the　same　environment　last　time.　Because　the　initialization　mechanism　based　on　the　MNN　does　not　need　the　solutions　in　recent　time,　the　proposed　algorithm　can　also　solve　dynamic　multi-objective　optimization　problems　with　a　dramatically　and　irregularly　changing　Pareto　set.　The　proposed　algorithm　is　tested　on　a　variety　of　test　instances　with　different　dynamic　characteristics　and　difficulties.　The　comparisons　of　experimental　results　with　other　state-of-the-art　algorithms　demonstrate　that　the　proposed　algorithm　is　promising　for　dealing　with　dynamic　multi-objective　optimization.