This　study　proposes　a　novel　DE　variant　for　global　optimizations　based　on　both　top　collective　information　and　p-best　information　(called　CIpBDE).　A　combined　mutation　strategy　(CIpBM)　takes　advantage　of　the　mutation　strategies　＂target-to-cipbest/1＂　and　＂target-to-pbest/1＂　is　introduced　trying　to　escape　from　stuck　of　local　optima.　A　modified　crossover　operation　(CIpBX)　is　proposed　to　handle　the　stagnation　of　DE.　CIpBX　adopts　a　collective　vector　or　top　p-best　individual　based　on　probability　to　execute　crossover　operation　when　stagnation　occurs.　An　improved　parameter　adaptation　strategy　is　figured　out　to　adaptability　to　adjust　the　parameters　crossover　probability　and　scale　factor　value　in　each　generation.　To　evaluate　the　performance　of　CIpBDE,　comprehensive　experiments　are　conducted　on　the　CEC2013　benchmark　test　suit　with　28　functions.　Experimental　results　show　that　CIpBDE　outperforms　the　seven　state-of-the-art　DE　variants.　What＇s　more,　we　also　apply　CIpBDE　to　the　feature　selection　problem.　Compared　results　on　several　standard　data　sets　indicate　that　CIpBDE　outperforms　the　four　comparing　algorithms　in　terms　of　classification　accuracy.