The　component　pick-and-place　sequence　is　one　of　the　key　factors　to　affect　the　working　efficiency　of　the　surface　mounting　machine　in　the　printed　circuit　board　assembly.　In　this　paper,　an　improved　Shuffled　Frog-leaping　Algorithm　was　presented　by　improving　the　basic　Shuffled　Frog-leaping　Algorithm　(SFLA)　with　the　strategy　of　letting　all　frogs　taking　part　in　memetic　evolution　and　adding　the　self-variation　behavior　to　the　frog.　The　objective　function　of　component　pick-and-place　sequence　of　the　gantry　multi-head　component　surface　mounting　machine　was　established.　Parameters　selection　is　critical　for　SFLA.　In　this　study,　Three-way　ANOVA　was　used　in　parameters　analyzing　of　the　new　improved　SFLA.　The　parameters　like　memeplex　numbers　m,　the　frogs＇　number　P　and　local　evolution　numbers　i(part)　were　found　having　notable　effects　on　the　mounting　time　(time　spent　for　components　picking　and　placing),　but　the　interactions　among　these　parameters　were　not　obvious.　Multiple　comparison　procedures　were　adopted　to　determine　the　best　parameter　settings.　In　order　to　test　the　performance　of　the　new　algorithm,　several　experiments　were　carried　out　to　compare　the　performance　of　improved　SFLA　with　the　basic　SFLA　and　the　genetic　algorithm　(GA)　in　solving　the　component　pick-and-place　sequence　optimization　problems.　The　experiment　results　indicate　that　improved　SFLA　can　solve　the　optimization　problem　efficiently　and　outperforms　SFLA　and　GA　in　terms　of　convergence　accuracy,　although　more　CPU　time　is　undeniably　needed.　(C)　2014　Elsevier　Ltd.　All　rights　reserved.