In　the　realm　of　existing　intelligent　drip　irrigation　control　systems,　traditional　PID　control　encounters　challenges　in　delivering　satisfactory　control　outcomes,　primarily　owing　to　issues　related　to　non-linearity,　time-varying　behavior,　and　hysteresis.　In　order　to　solve　the　problem　of　the　unstable　operation　of　the　drip　irrigation　system　in　an　intelligent　irrigation　system,　this　paper　proposes　chaotic　beetle　swarm　optimization　(CBSO)　based　on　the　BAS　(beetle　antennae　search)　longicorn　search　algorithm,　with　inertial　weights,　variable　learning　factors,　and　logistic　chaos　initialization　improving　global　search　capabilities.　This　was　accomplished　by　formulating　the　optimization　objective,　which　involved　integrating　the　control　input＇s　time　integral　term,　the　square　term,　and　the　absolute　value　of　the　error.　Subsequently,　PID　parameter　tuning　was　performed.　In　order　to　verify　the　actual　effect　of　the　CBSO　algorithm　on　the　PID　drip　irrigation　control　system,　MATLAB　was　used　to　simulate　and　compare　PID　control　optimized　by　the　GA　algorithm,　PSO　algorithm,　and　BSO　(beetle　search　optimization)　algorithm.　The　results　show　that　PID　control　based　on　CBSO　optimization　has　a　short　response　time,　small　overshoot,　and　no　oscillation　in　the　steady　state　process.　The　performance　of　the　controller　is　improved,　which　provides　a　basis　for　PID　parameter　setting　for　a　drip　irrigation　control　system.