Quantum　particle　swarm　optimization　(QPSO)　is　a　swarm　intelligence　method　that　has　been　successfully　applied　to　solve　a　wide　scope　of　electromagnetic　inverse　problems.　The　method　encounters　into　local　optima　and　insufficient　diversity　at　the　later　phase　of　optimization.　To　address　this　type　of　issue,　a　new　methodology　is　used　to　select　the　fittest　particle,　and　a　novel　mutation　mechanism　is　introduced,　in　which　a　mutation　technique　is　applied　on　the　global　best　particle　to　avoid　the　population　from　assembling　and　facilitating　the　individual　to　avoid　the　local　optimum　easily.　In　addition,　a　parameter　updating　strategy　is　proposed,　which　facilitates　the　optimizer　to　maintain　a　good　balance　between　local　and　global　searches.　To　demonstrate　the　merit　and　efficiency　of　the　proposed　methodology,　the　evaluated　results　from　the　case　studies　are　presented.