In　this　paper,　we　propose　a　new　Bi-Population　QUasi-Affine　TRansformation　Evolution　(BP-QUATRE)　algorithm　for　global　optimization.　The　proposed　BP-QUATRE　algorithm　divides　the　population　into　two　subpopulations　with　sort　strategy,　and　each　subpopulation　adopts　a　different　mutation　strategy　to　keep　the　balance　between　the　fast　convergence　and　population　diversity.　What　is　more,　the　proposed　BP-QUATRE　algorithm　dynamically　adjusts　scale　factor　with　a　linear　decrease　strategy　to　make　a　good　balance　between　exploration　and　exploitation　capability.　We　compare　the　proposed　algorithm　with　two　QUATRE　variants,　PSO-IW,　and　DE　algorithms　on　the　CEC2013　test　suite.　The　experimental　results　demonstrate　that　the　proposed　BP-QUATRE　algorithm　outperforms　the　competing　algorithms.　We　also　apply　the　proposed　algorithm　to　dynamic　deployment　in　wireless　sensor　networks.　The　simulation　results　show　that　the　proposed　BP-QUATRE　algorithm　has　better　coverage　rate　than　the　other　competing　algorithms.　©　2019,　The　Author(s).