The　multi-objective　evolutionary　algorithm　optimization　(MOEA)　is　a　challenging　but　critical　approach　for　tackling　complex　data　analysis　problems.　However,　prevailing　MOEAs　often　rely　on　single　strategies　to　obtain　optimal　solutions,　leading　to　concerns　such　as　premature　convergence　and　insufficient　population　diversity,　particularly　in　high-dimensional　data　scenarios.　In　this　paper,　we　propose　a　novel　adversarial　population　generation　algorithm,　APG-SMOEA,　which　synergistically　combines　the　benefits　of　MOEAs　and　Generative　Adversarial　Networks　(GANs)　to　address　these　limitations.　In　order　to　balance　the　efficiency　and　quality　of　offspring　selection,　we　introduce　an　adaptive　population　entropy　strategy,　which　includes　control　parameters　based　on　population　entropy　and　a　learning　pool　for　storing　and　retrieving　optimal　solutions.　Additionally,　we　attempt　to　alleviate　the　training　complexity　and　model　collapse　problems　common　in　GANs　with　APG-SMOEA.　Experimental　results　on　benchmarks　demonstrate　that　the　proposed　algorithm　is　superior　to　the　existing　algorithms　in　terms　of　solution　quality　and　diversity　of　low-dimensional　or　high-dimensional　complex　data.