The　evolutional　motivated　particle　swarm　optimisation　(PSO)　has　been　widely　employed　in　various　scientific　areas,　and　there　has　been　plenty　of　contribution　on　the　modification　and　improvement　of　PSO.　Recently,　a　quantum　behaviour　inspired　optimisation　algorithm　(QPSO)　was　developed　by　modelling　a　Delta　potential　well　in　quantum　space,　which　shows　better　performance　in　global　search　ability　and　convergence　precision　compared　with　the　original　PSO　algorithm.　In　this　paper,　based　on　the　principle　of　QPSO,　we　proposed　a　dynamic　search　strategy　fused　with　chaos　map　to　strengthen　the　ability　of　escaping　from　local　optima,　and　replaced　the　attractor　with　beta　distribution　for　faster　convergence　speed.　We　first　compared　this　improved　algorithm　(DCQPSO)　with　PSO　and　QPSO　on　general　optimisation　benchmark　functions.　Then,　from　the　point　view　of　application,　we　also　achieved　a　simplicity-oriented　human　hand　kinematics　tracking　system　by　leveraging　DCQPSO,　which　can　be　further　served　in　human　computer　interaction　(HCI).　Indicated　by　the　experiments　result,　DCQPSO　outperforms　either　traditional　PSO　or　QPSO　algorithm,　and　it　can　be　well　qualified　with　optimisation　task　in　hand　kinematics　tracking.