Defect　imaging　algorithms　play　an　important　role　in　Lamb　waves　based　researches　of　nondestructive　testing　(NDT)　and　structural　health　monitoring　(SHM).　In　classical　algorithms,　the　location　or　distribution　of　defects　is　visualized　through　mapping　the　amplitude　or　phase　information　of　signals　gotten　by　multiple　inspection　pairs　from　the　time　domain　to　every　discrete　spatial　grid　of　plates.　It　is　time-consuming　in　the　detection　of　plates　with　large　size　and　many　transducers.　Transforming　the　defect　imaging　problem　into　a　scattering　source　search　problem,　an　intelligent　defect　localization　algorithm　was　proposed　for　NDT　and　SHM　with　the　Lamb　waves　and　sparse　array.　In　the　algorithm,　the　elliptic　trajectory-dependent　individuals　of　every　inspection　pair　were　extracted　first,　then　the　defect　position　was　identified　by　analyzing　the　distribution　of　individuals　these　located　at　the　intersection　of　multiply　elliptic　trajectories.　Considering　the　fuzzy　and　diversity　characteristics　in　the　detection　of　defects,　a　fuzzy　control　parameter　and　an　adaptive　individual　updating　strategy　based　on　the　k-means　algorithm　were　introduced　to　ensure　the　robustness　of　the　algorithm.　The　effectiveness　of　the　proposed　algorithm　was　verified　by　numerical　models　and　experiments.　The　influences　of　the　fuzzy　control　parameter　and　the　individual　updating　strategy　on　the　performance　of　the　algorithm　were　analyzed　furthermore.　©　2021　by　ASME.