The　development　and　application　of　photovoltaic　(PV)　systems　are　becoming　increasingly　more　important　as　the　global　need　for　energy　resources　expands　and　environmental　protection　becomes　more　highly　valued.　Parameters　of　PV　models　can　be　identified　by　measuring　their　current-voltage　(I-V)　characteristic　curves.　Identifying　these　parameters　quickly,　accurately　and　reliably　is　critical　in　determining　the　operating　status　of　in　situ　PV　arrays　and,　in　turn,　optimising　solar　energy　conversions.　To　achieve　both　fast　and　accurate　parameter　identification　with　high　reliability,　a　new　algorithm　called　algorithm　based　on　SSO　and　Nelder-Mead　simplex　(NMS)　(SSSO)　based　on　the　simplified　swarm　optimisation　(SSO)　and　the　NMS　is　proposed　in　this　study.　To　demonstrate　the　performance　of　SSSO　in　identifying　solar　cell　system　parameters,　its　performance　on　the　single　diode　model　and　the　double　diode　model　was　compared　with　existing　algorithms　in　terms　of　both　fitness　value　and　run　time.　The　experiment　results　indicate　that　SSSO　outperformed　the　compared　algorithms　in　both　run　time　and　standard　deviation　of　fitness　value.