In　optimization　and　decision-making,　multi-objective　optimization　has　emerged　as　a　pivotal　challenge.　Over　the　past　three　decades,　the　concerted　efforts　of　scholars　and　practitioners　across　various　disciplines　have　significantly　advanced　the　study　and　implementation　of　Multi-Objective　Evolutionary　Algorithms　(MOEAs).　MOEAs　stand　at　the　forefront　of　multi-objective　decision-making　methodologies,　marking　a　vibrant　area　of　inquiry　within　evolutionary　computation.　This　body　of　work　categorizes　MOEAs　into　three　distinct　streams:　Decomposition-based　MOEA　algorithms,　Dominant　relationship-based　MOEA　algorithms,　and　Evaluation　index-based　MOEA　algorithms.　Focusing　specifically　on　dominance-based　MOEAs,　this　study　integrates　them　with　chaotic　evolution　(CE)　strategies　to　enhance　the　efficacy　of　multi-objective　optimization　processes.　Through　comparative　analysis　against　traditional　algorithms,　the　newly　proposed　chaotic　MOEA　demonstrates　superior　optimization　performance,　thereby　setting　a　robust　groundwork　for　the　continuous　evolution　and　application　of　MOEAs.