Parameter　estimation　techniques　based　on　chirplet　models　and　intelligent　algorithms　can　realize　the　simultaneous　multi-information　extraction　of　signals.　They　have　attracted　considerable　attention　for　the　processing　of　Lamb　wave　signals　to　detect　defects　and　evaluate　the　material　properties.　Influenced　by　their　dispersive　nature,　Lamb　wave　signals　possess　nonlinear　instantaneous　frequencies　and　asymmetric　envelopes.　However,　the　classical　chirplet　models　are　established　with　either　Gaussian　windows　or　linear　chirps.　They　are　inadequate　for　characterizing　the　dispersion　features　of　ultrasonic　signals　whose　excitations　are　modulated　by　Hanning　windows.　In　our　previous　work,　a　nonlinear　Hanning-windowed　chirplet　(NHWC)　model　with　nine　parameters　was　proposed　to　realize　the　full　characterization　of　waveforms.　However,　the　large　number　of　parameters　limits　its　application.　A　simple　NHWC　model　with　seven　parameters　was　designed　by　submitting　the　same　nonlinear　phase　modulation　term　into　the　Hanning-windowed　sine　function　in　this　paper.　Furthermore,　a　real-coded　multi-objective　genetic　algorithm　was　developed　to　realize　the　parameter　estimation　of　signals　by　combining　a　clustering　algorithm　and　the　NHWC　model.　Different　strategies　were　adopted　to　ensure　the　convergence　of　the　algorithm.　The　maximum　extreme　values　were　adopted　to　realize　adaptive　discretization　of　the　search　space　and　the　updating　of　parameters.　The　parameters　in　the　NHWC　models　were　divided　into　implicit　and　explicit　parts,　and　different　strategies　were　applied　to　update　them.　The　clustering　algorithm　and　a　sorting　combination　method　were　employed　to　generate　a　Pareto　set.　Experimental　results　showed　that　the　parameter　estimation　with　the　simplified　NHWC　model　exhibited　a　more　robust　performance　than　that　of　the　model　that　contained　nine　parameters　for　characterizing　the　Lamb　wave　signals　with　or　without　the　dispersion　features.　The　arrival　time,　amplitude,　and　instantaneous　frequencies　of　wave　packets　were　identified　with　the　parameter　estimation　technique.　©　2020　Elsevier　B.V.