Developing　metaheuristic　algorithms　has　been　paid　more　recent　attention　from　researchers　and　scholars　to　address　the　optimization　problems　in　many　fields　of　studies.　This　paper　proposes　a　novel　adaptation　of　the　multi-group　quasi-affine　transformation　evolutionary　algorithm　for　global　optimization.　Enhanced　population　diversity　for　adaptation　multi-group　quasi-affine　transformation　evolutionary　algorithm　is　implemented　by　randomly　dividing　its　population　into　three　groups.　Each　group　adopts　a　mutation　strategy　differently　for　improving　the　efficiency　of　the　algorithm.　The　scale　factor　F　of　mutations　is　updated　adaptively　during　the　search　process　with　the　different　policies　along　with　proper　parameter　to　make　a　better　trade-off　between　exploration　and　exploitation　capability.　In　the　experimental　section,　the　CEC2013　test　suite　and　the　node　localization　in　wireless　sensor　networks　were　used　to　verify　the　performance　of　the　proposed　algorithm.　The　experimental　results　are　compared　results　with　three　quasi-affine　transformation　evolutionary　algorithm　variants,　two　different　evolution　variants,　and　two　particle　swarm　optimization　variants　show　that　the　proposed　adaptation　multi-group　quasi-affine　transformation　evolutionary　algorithm　outperforms　the　competition　algorithms.　Moreover,　analyzed　results　of　the　applied　adaptation　multi-group　quasi-affine　transformation　evolutionary　for　node　localization　in　wireless　sensor　networks　showed　that　the　proposed　method　produces　higher　localization　accuracy　than　the　other　competing　algorithms.　©　2019　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.