Quantum　inspired　particle　swarm　optimization　(QPSO)　is　widely　used　global　convergence　algorithm　for　complex　design　problems.　But　it　may　trap　into　local　optima　due　to　premature　convergence　because　of　insufficient　diversity　at　the　later　stage　of　search　process.　In　this　regard,　to　intensify　the　QPSO　performance　in　preventing　premature　convergence　to　local　optima.　This　work　presents　a　novel　QPSO　approach　using　student　t　probability　distribution　method　with　mutation　operator　on　particle　with　global　best　position.　In　addition,　a　new　dynamic　control　parameter　is　proposed　to　tradeoff　between　the　exploration　and　exploitation　searches.　The　proposed　method　will　intensify　the　improvement　in　its　convergence　behavior　and　solution　quality.　The　proposed　improve　QPSO　called　IQPSO　is　tested　on　an　electromagnetic　design　problem　namely,　the　TEAM　workshop　benchmark　problem　22.　The　experimental　results　showcase　the　merit　and　efficiency　of　the　proposed　method.