Particle　swarm　optimizer　is　one　of　the　searched　based　stochastic　technique　that　has　a　weakness　of　being　trapped　into　local　optima.　Thus,　to　tradeoff　between　the　local　and　global　searches　and　to　avoid　premature　convergence　in　PSO,　a　new　dynamic　quantum-based　particle　swarm　optimization　(DQPSO)　method　is　proposed　in　this　work.　In　the　proposed　method　a　beta　probability　distribution　technique　is　used　to　mutate　the　particle　with　the　global　best　position　of　the　swarm.　The　proposed　method　can　ensure　the　particles　to　escape　from　local　optima　and　will　achieve　the　global　optimum　solution　more　easily.　Also,　to　enhance　the　global　searching　capability　of　the　proposed　method,　a　dynamic　updated　formula　is　proposed　that　will　keep　a　good　balance　between　the　local　and　global　searches.　To　evaluate　the　merit　and　efficiency　of　the　proposed　DQPSO　method,　it　has　been　tested　on　some　well-known　mathematical　test　functions　and　a　standard　benchmark　problem　known　as　Loney’s　solenoid　design.　©　ACES