The　performance　of　photovoltaic　(PV)　cell　is　affected　by　the　model　structure　and　corresponding　parameters.　However,　these　parameters　are　adjustable　and　variable,　which　play　an　available　role　in　regarding　to　the　efficiency　and　effectiveness　of　PV　generation.　Due　to　strong　non-linear　characteristics,　existing　PV　model　parameters　identification　methods　cannot　easily　obtain　accurate　solutions.　To　tackle　this,　this　paper　proposes　an　adaptive　differential　evolution　algorithm　with　the　dynamic　opposite　learning　strategy　(DOL),　named　DOLADE.　In　DOLADE,　the　opposite　learning　method　expands　the　current　elite　population　and　the　population　of　poor　performance,　improving　the　particles＇　exploration　capability.　In　the　process　of　particles　work,　the　searching　area　of　particles　is　adjusting　dynamically　so　that　the　particles＇　exploitation　capability　is　enhanced.　The　experimental　data　of　different　types　of　PV　are　tested,　respectively.　Three　PV　models　are　used　to　verify　the　new　strategy＇s　accuracy　and　effectiveness.　The　proposed　DOLADE　is　compared　with　several　general　advanced　algorithms,　and　comprehensive　experimental　results　are　demonstrated.　The　results　illustrate　that　DOLADE　well　extracts　optimal　parameters　for　each　PV　cell　model　and　brought　great　competition　in　terms　of　accuracy,　reliability,　and　computational　efficiency　in　solving　the　problem.