Although　the　particle　swarm　optimization　algorithm　has　the　advantages　of　fast　convergence,　easy　to　use　and　strong　versatility,　the　algorithm　also　has　the　defects　of　low　search　precision,　poor　local　search　ability　and　easy　to　fall　into　local　optimal　solution.　Therefore,　this　paper　proposes　a　particle　swarm　optimization　algorithm　based　on　dynamic　adaptive　and　chaotic　search　to　ensure　the　global　search　ability　of　the　particle　swarm　while　avoiding　falling　into　the　local　optimal　solution.　The　experimental　results　show　that　compared　with　the　comparison　algorithm,　the　DACSPSO　has　stronger　global　search　ability,　higher　convergence　precision,　and　can　effectively　avoid　premature　convergence.　©　2019　Published　under　licence　by　IOP　Publishing　Ltd.