The　particle　swarm　optimization　(1∗80)　method　can　have　difficulty　reaching　local　minima　and　have　difficulty　optimizing　high-dimensional　nonlinear　problems.　In　order　to　address　these　concerns,　we　propose　an　a　daptive　particle　swarm　optimization　algorithm　with　local　search.　The　core　premise　of　the　algorithm　is　to　adjust　the　algorithm　parameters　dynamically　based　on　the　population　distribution　information　and　to　increase　population　diversity　by　incorporating　a　chaos　mutation　mechanism.　A　mechanism　is　added　to　strengthen　the　local　search　ability　of　the　algorithm.　The　optimization　results　of　six　benehruarking　functions　show　that　the　algorithm　exhibits　better　optimization　performance.　We　also　apply　the　algorithm　to　the　optimization　of　two　actual　network　cases;　the　Hanoi　network　and　the　New　York　network.　The　results　show　that　the　algorithm　provides　a　better　search　precision　and　faster　convergence　speed　than　other　algorithms.