This　paper　presents　a　new　interactive　neural　network　for　solving　constrained　multiobjective　optimization　problems.　The　constrained　multi-objective　optimization　problem　is　reformulated　into　two　constrained　single　objective　optimization　problems　and　two　neural　networks　are　designed　to　obtain　the　optimal　weight　and　the　optimal　solution　of　the　two　optimization　problems　respectively.　The　proposed　algorithm　has　a　low　computational　complexity　and　is　easy　to　be　implemented.　Moreover,　the　proposed　algorithm　is　well　applied　to　the　design　of　digital　filters.　Computed　results　illustrate　the　good　performance　of　the　proposed　algorithm.　©　(2012)　Trans　Tech　Publications,　Switzerland.