To　solve　the　problem　that　the　feature　weights　are　difficult　to　quantify　accurately,　a　hybrid　algorithm　based　on　grey　wolf　optimizer　(GWO)　algorithm　and　bird　swarm　algorithm　(BSA)　was　proposed　to　optimize　the　feature　weights.　First,　Chebyshev　map,　opposition鄄based　learning　and　elitism　strategy　were　used　to　initialize　the　population　of　the　hybrid　algorithm.　Second,　the　location　updating　formula　of　GWO　algorithm　and　the　foraging　behavior　of　BSA　were　combined　as　the　improved　location　updating　strategy　of　the　algorithm　for　local　search.　Then,　the　vigilance　behavior　and　flight　behavior　of　BSA　were　integrated　into　the　hybrid　algorithm　to　obtain　a　balance　strategy　for　global　search.　A　convergent　grey　wolf　and　bird　swarm　algorithm　(GWBSA)　was　obtained,　and　the　feature　weights　were　optimized　through　the　iteration　of　GWBSA.　Experiments　were　carried　out　by　using　benchmark　functions　and　standard　classification　data　sets,　respectively.　Compared　with　the　genetic　algorithm,　the　ant　lion　algorithm　and　other　algorithms,　the　GWBSA　has　fast　convergence　speed　and　is　hard　to　fall　into　local　optimum,　which　can　improve　the　solution　quality　of　pattern　classification　problems.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.