Quantum　inspired　particle　swarm　optimization　(QPSO)　stimulated　by　perceptions　from　particle　swarm　optimization　and　quantum　mechanics　is　a　stochastic　optimization　method.　Although,　it　has　shown　good　performance　in　finding　the　optimal　solution　to　many　electromagnetic　problems.　However,　sometimes　it　falls　to　local　optima　when　dealing　with　hard　optimization　problems.　Thus,　to　preserve　a　good　balance　between　local　and　global　searches　to　avoid　premature　convergence　in　quantum　particle　swarm　optimization,　this　paper　proposed　three　enhancements　to　the　original　QPSO　method,　the　proposed　method　is　called　modified　quantum　particle　swarm　optimization　(MQPSO)　algorithm.　Firstly,　a　novel　selection　technique　is　introduced　that　will　choose　the　best　particle　among　the　population　within　the　search　domain　to　achieve　a　high-performance　exploration.　Secondly,　a　new　mutation　method　is　used　to　preserve　the　easiness　of　available　QPSOs.　Also,　a　dynamic　parameter　strategy　is　proposed　for　further　facilitating　the　algorithm　and　tradeoff　between　exploration　and　exploitation　searches.　The　experimental　results　obtained　by　solving　standard　benchmark　functions　and　an　electromagnetic　design　problem　which　is　the　superconducting　magnetic　energy　storage　(SMES)　system　available　in　both　three　parameters　and　eight　parameters　problems　are　reported　to　showcase　the　usefulness　of　the　proposed　approach.