Intelligent　defect　location　algorithms　based　on　the　times-of-flight　(ToFs)　of　Lamb　waves　are　attractive　for　nondestructive　testing　(NDT)　and　structural　health　monitoring　(SHM)　of　structures　with　large　geometric　sizes.　Unlike　the　classical　imaging　algorithm　based　on　projecting　the　amplitude　information　of　scattering　signals　into　a　discrete　spatial　grid　on　the　structure　via　their　propagation　characteristics,　intelligent　defect　location　algorithms　are　more　efficient　in　specific　applications.　In　our　previous　work,　an　intelligent　algorithm　for　the　location　of　defects　in　plates　was　proposed　by　considering　the　statistical,　diversity,　and　fuzzy　characteristics　of　the　classical　defect　location　algorithm.　This　approach　can　realize　the　efficient　location　of　different　defects　under　a　suitable　parameter　selection.　However,　interfering　components　remain　in　the　results,　which　decreases　the　detection　resolution.　Because　the　measurement　uncertainty　is　directly　related　to　the　time,　an　optimized　intelligent　location　algorithm　is　provided　for　the　efficient　defect　location　with　Lamb　waves　and　a　sparse　transducer　array　in　this　study.　The　defect　position　is　identified　with　high　resolution　by　analyzing　the　distribution　of　individuals.　Several　specific　data　and　a　fuzzy　control　parameter　are　introduced　to　the　proposed　algorithm.　The　K-means　algorithm　was　adopted　to　realize　the　adaptive　updating　of　individuals.　The　influence　of　parameter　values　on　the　detection　results　was　analyzed.　A　combined　analysis　of　the　individuals　was　provided　to　ensure　the　detection　robustness　by　eliminating　the　influence　of　fuzzy　control　parameters　on　the　detection.　Compared　with　the　elliptic　imaging　algorithm,　the　intelligent　defect　location　algorithm　has　higher　location　resolution　and　executes　approximately　65　times　faster.