Continuous-flow　microfluidic　biochip　is　a　revolutionary　technology　for　automation　and　miniaturization　of　biochemical　experiments.　As　one　of　the　key　links　in　the　automatic　design　of　continuous　microfluidic　biochips,　the　control　pattern　assignment　problem　of　multiplexers　is　an　NP-hard　combinatorial　optimization　problem.　The　existing　particle　swarm　optimization　algorithm　for　control　pattern　assignment　problem　has　the　disadvantages　of　falling　into　local　optimal　solution　prematurely,　slow　convergence　speed,　and　poor　stability　of　the　algorithm.　In　this　paper,　control　pattern　assignment　algorithm　based　on　hybrid　discrete　particle　swarm　optimization　for　continuous-flow　microfluidic　biochips　is　proposed.　First,　in　order　to　accelerate　the　convergence　speed　of　the　proposed　algorithm　and　avoid　falling　into　a　local　optimum　prematurely,　a　discrete　adaptive　region　search　strategy　is　proposed.　Second,　the　stability　of　the　proposed　algorithm　is　improved　by　a　sample-based　social　learning　mechanism.　Third,　the　optimal　combination　of　the　important　parameters　in　the　adaptive　region　search　strategy　is　selected　by　equidistant　sampling　to　further　improve　the　results.　The　final　experimental　results　show　that　the　proposed　algorithm　optimizes　the　number　of　valves　by　an　average　of　19.01%,　and　improves　the　stability　of　the　algorithm　by　29.18%,　and　then　performs　well　in　practical　biochemical　applications.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.