DNA　computing　has　efficient　computational　power,　but　requires　high　requirements　on　the　DNA　sequences　used　for　coding,　and　reliable　DNA　sequences　can　effectively　improve　the　quality　of　DNA　encoding.　And　designing　reliable　DNA　sequences　is　an　NP　problem,　because　it　requires　finding　DNA　sequences　that　satisfy　multiple　sets　of　conflicting　constraints　from　a　large　solution　space.　To　better　solve　the　DNA　sequence　design　problem,　we　propose　an　improved　bare　bones　particle　swarm　optimization　algorithm　(IBPSO).　The　algorithm　uses　dynamic　lensing　opposition-based　learning　to　initialize　the　population　to　improve　population　diversity　and　enhance　the　ability　of　the　algorithm　to　jump　out　of　local　optima;　An　evolutionary　strategy　based　on　signal-to-noise　ratio(SNR)　distance　is　designed　to　balance　the　exploration　and　exploitation　of　the　algorithm;　Then　an　invasive　weed　optimization　algorithm　with　niche　crowding(NCIWO)　is　used　to　eliminate　low-quality　solutions　and　improve　the　search　efficiency　of　the　algorithm.　In　addition,　we　introduce　the　triplet-bases　unpaired　constraint　to　further　improve　the　quality　of　DNA　sequences.　Finally,　the　effectiveness　of　the　improved　strategy　is　demonstrated　by　ablation　experiments;　and　the　DNA　sequences　designed　by　our　algorithm　are　of　higher　quality　compared　with　those　generated　by　the　six　advanced　algorithms.