The　parameters　of　solar　cells　models　have　an　effect　on　the　simulation　of　solar　cells　and　can　be　applied　to　monitor　the　working　condition　and　diagnose　potential　faults　for　photovoltaic　(PV)　modules　in　a　PV　system.　To　accurately　and　efficiently　extract　the　optimal　parameters　of　solar　cells　in　a　limited　CPU　run　time,　a　modified　simplified　swarm　optimization　(MSSO)　algorithm　is　presented　for　the　single　diode　and　double　diode　models　by　minimizing　the　least　square　error　between　the　calculated　and　experimental　data.　In　MSSO,　a　new　one-variable-update　mechanism　and　survival-of-the-fittest　policy　are　applied　to　enhance　the　ability　of　traditional　SSO.　To　investigate　the　performance　of　MSSO,　comparative　studies　with　other　well-known　optimization　algorithms,　i.e.,　SSO,　artificial　bee　colony　(ABC)　and　simplified　bird　mating　optimizer　(SBMO),　are　presented,　and　extensive　computational　results　are　shown.　The　statistical　data　indicate　that　the　MSSO　method　has　the　best　performance　among　these　methods　in　terms　of　efficiency,　robustness　and　accuracy.　Moreover,　the　current　vs.　voltage　characteristics　of　the　parameters　extracted　by　MSSO　coincide　well　with　those　of　experimental　data.　©　2017　Elsevier　Ltd