With　the　rapid　development　of　mobile　technology,　mobile　applications　have　increasing　requirements　for　computational　resources,　and　mobile　devices　can　no　longer　meet　these　requirements.　Mobile　edge　computing　(MEC)　has　emerged　in　this　context　and　has　brought　innovation　into　the　working　mode　of　traditional　cloud　computing.　By　provisioning　edge　server　placement,　the　computing　power　of　the　cloud　center　is　distributed　to　the　edge　of　the　network.　The　abundant　computational　resources　of　edge　servers　compensate　for　the　lack　of　mobile　devices　and　shorten　the　communication　delay　between　servers　and　users.　Constituting　a　specific　form　of　edge　servers,　cloudlets　have　been　widely　studied　within　academia　and　industry　in　recent　years.　However,　existing　studies　have　mainly　focused　on　computation　offloading　for　general　computing　tasks　under　fixed　cloudlet　placement　positions.　They　ignored　the　impact　on　computation　offloading　results　from　cloudlet　placement　positions　and　data　dependencies　among　mobile　application　components.　In　this　paper,　we　study　the　cloudlet　placement　problem　based　on　workflow　applications　(WAs)　in　wireless　metropolitan　area　networks　(WMANs).　We　devise　a　cloudlet　placement　strategy　based　on　a　particle　swarm　optimization　algorithm　using　genetic　algorithm　operators　with　the　encoding　library　updating　mode　(PGEL),　which　enables　the　cloudlet　to　be　placed　in　appropriate　positions.　The　simulation　results　show　that　the　proposed　strategy　can　obtain　a　near-optimal　cloudlet　placement　scheme.　Compared　with　other　classic　algorithms,　this　algorithm　can　reduce　the　execution　time　of　WAs　by　15.04–44.99%.　©　2022　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.