Particle　swarm　optimization　(PSO)　has　many　advantages　such　as　fewer　parameters,　faster　convergence　and　easy　implementation;　however,　it　is　also　prone　to　fall　into　local　optimum.　Because　inertia　weight　parameters　can　increase　the　diversity　of　particles　and　effectively　overcome　this　problem,　a　large　number　of　studies　have　done　on　inertia　weight　strategy　since　it　was　put　forward,　and　many　different　improvement　strategies　have　been　put　forward,　but　these　improvement　strategies　still　do　not　make　full　use　of　the　multi-dimensional　information　of　particles,　resulting　in　limited　improvement　of　PSO　performance.　In　this　study,　on　the　one　hand,　based　on　the　summary　of　current　inertial　weight　strategies,　aiming　at　the　problems　existing　in　the　classification　of　inertial　weight　strategies,　the　inertial　weight　strategies　are　reclassified.　According　to　the　new　classification　methods,　the　inertial　weight　strategies　are　divided　into　four　categories,　including　＂no　variable　with　iteration＂,　＂single　variable　with　iteration＂,　＂double　variables　with　iteration＂　and　＂triple　variables　with　iteration＂　inertia　weight.　On　the　other　hand,　in　view　of　the　shortcomings　of　the　existing　inertial　weight　improvement　strategies,　this　study　proposes　a　multi-information　fusion　＂triple　variables　with　iteration＂　inertia　weight　PSO　algorithm　(MFTIWPSO),　which　combines　the　multi-dimensional　information　of　particle＇s　time　(or　iteration),　particle　and　dimension　(or　space).　In　order　to　test　the　optimization　performance　of　our　proposed　MFTIWPSO,　the　benchmark　functions　are　used　to　test　the　optimization　performance,　and　then　the　algorithm　is　used　to　optimize　the　parameters　of　machine　learning　classifier　and　classify　the　biological　datasets.　The　results　of　two　tests　show　that　the　proposed　MFTIWPSO　algorithm　has　better　optimization　performance　than　other　optimization　algorithms.　(C)　2019　Elsevier　B.V.　All　rights　reserved.