In　this　paper,　we　propose　a　new　Competitive　Learning　QUasi　Affine　TRansformation　Evolutionary　(CLQUATRE)　algorithm　for　Global　Optimization　and　its　application　in　Capacitated　Vehicle　Routing　Problem　(CVRP).　In　the　proposed　CL-QUATRE,　the　population　is　divided　into　two　subpopulations　(i.e.,　winner　and　loser)　with　a　pair　wise　competition　mechanism.　Each　subpopulation　utilizes　different　mutation　strategy　to　reserve　the　population　diversity　and　improve　convergence　speed.　The　winner　evolves　with　a　mutation　strategy　＂QUATRE/best/1＂,　whereas　the　loser　evolves　with　a　modified　mutation　strategy　＂QUATRE/target-to-best-win　ner/1＂,　which　learns　from　winner　subpopulation　to　make　the　algorithm　more　efficient.　Meanwhile,　a　scale　factor　updating　method,　called　stochastic　scale　factor,　is　introduced　into　the　proposed　CL-QUATRE　algorithm　to　jump　out　of　the　local　optima　and　avoid　falling　into　stagnation.　With　these　modifications,　the　proposed　algorithm　can　achieve　good　balance　between　exploration　and　exploitation　capability.　We　compare　the　proposed　algorithm　with　four　QUATRE　variants,　four　DE　variants,　and　four　PSO　variants　on　CEC2013　test　suite,　CEC2014　test　suite　and　two　CVRP　benchmarks.　The　experimental　results　demonstrate　that　the　CL-QUATRE　algorithm　achieves　better　or　competitive　performance.