We　propose　an　extension　of　the　chaotic　evolution　algorithm　into　the　discrete　domain　to　address　combinatorial　optimization　problems.　In　this　study,　we　leverage　the　discrete　chaotic　evolution　algorithm　to　tackle　the　Traveling　Salesman　Problem　(TSP)　for　assessment　purposes.　The　chaotic　evolution　algorithm　exploits　the　ergodicity　of　chaos　to　facilitate　the　search　process　within　the　optimization　algorithm.　It　incorporates　a　mathematical　mechanism　into　the　iterative　evolution　process,　simulating　ergodic　motion　within　a　search　space　based　on　a　simple　principle.　To　manage　the　discrete　mutation　operation　within　the　chaotic　evolution　algorithm,　we　introduce　a　specifically　designed　chaotic　operation.　This　operation　is　tailored　for　its　application　in　solving　combinatorial　optimization　problems.　The　chaotic　sequence　plays　a　crucial　role　in　determining　the　mutation　location.　Our　evaluation　involves　the　comparison　of　our　proposed　discrete　chaotic　evolution　algorithm　with　the　outcomes　of　the　simulated　annealing　algorithm　and　the　tabu　search　algorithm.　The　assessment　serves　to　demonstrate　and　validate　that　the　discrete　chaotic　evolution　algorithm　yields　superior　optimization　performance　within　the　discrete　domain.　©　2024　IEEE.