简介：Basedonthetropicalcyclone(TC)observationsinthewesternNorthPacificfrom2000to2008,thispaperadoptstheparticleswarmoptimization(PSO)algorithmofevolutionarycomputationtooptimizeonecomprehensiveclassificationrule,andapplytheoptimizedclassificationruletotheforecastingofTCintensitychange.Intheprocessoftheoptimization,thestrategyofhierarchicalpruninghasbeenadoptedinthePSOalgorithmtonarrowthesearcharea,andthustoenhancethelocalsearchability,i.e.hierarchicalPSOalgorithm.TheTCintensityclassificationruleinvolvescoreattributesincluding12-HMWS,MPI,andRainratewhichplayvitalrolesinTCintensitychange.ThetestingaccuracyusingthenewminedrulebyhierarchicalPSOalgorithmreaches89.6%.ThecurrentstudyshowsthatthenovelclassificationmethodforTCintensitychangeanalysisbasedonhierarchicPSOalgorithmisnotonlyeasytoexplainthesourceofrulecoreattributes,butalsohasgreatpotentialtoimprovetheforecastingofTCintensitychange.