The　objective　of　the　research　is　to　extend　the　potential　of　the　standard　quantum　particle　swarm　optimization　(QPSO)　method　for　electromagnetic　inverse　problems.　As,　QPSO　trapped　into　local　optima　while　dealing　with　complex　design　problems.　In　order　to　address　this　type　of　issue,　to　avoid　from　trapping　into　local　optima　and　tradeoff　between　the　exploration　and　exploitation　searches,　a　novel　methodology　is　employed　which　includes　the　design　of　a　new　position　updating　formula,　the　introduction　of　a　novel　fitness　selection　methodology,　and　the　proposal　of　a　dynamic　parameter　updating　strategy.　Nevertheless,　the　evaluated　results　as　reported　have　revealed　that　the　proposed　modified　quantum-inspired　particle　swarm　optimization　method　for　global　optimization　and　electromagnetic　inverse　problems　can　find　better　outcomes　at　initial　stage　of　the　iterating　process　as　compared　with　other　tested　optimal　methods.