Community　detection　on　attributed　networks　is　a　method　to　discover　community　structures　within　attributed　networks.　By　applying　community　detection　on　attribute　networks,　we　can　better　understand　the　relationships　between　nodes　in　real-world　networks.　However,　current　algorithms　for　community　detection　on　attribute　networks　rely　on　hyper-parameters,　and　it　is　difficult　to　obtain　an　ideal　result　when　facing　networks　with　inconsistent　attributes　and　topology.　Consequently,　we　propose　an　Unsupervised　Multi-population　Evolutionary　Algorithm　(UMEA)　for　community　detection　in　attributed　networks.　This　algorithm　adds　edges　between　nodes　based　on　attribute　similarity,　allowing　it　to　combine　attribute　information　during　the　process　of　community　detection.　In　addition,　this　algorithm　determines　the　optimal　number　of　added　edges　autonomously　through　communication　and　learning　between　multiple　populations.　Furthermore,　we　propose　a　series　of　strategies　to　accelerate　population　convergence　for　the　locus-based　encoding.　Experiments　have　demonstrated　that　our　algorithm　outperforms　the　benchmark　algorithms　in　both　real　and　artificial　networks.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.