In　modern　circuit　design,　the　short-circuit　problem　is　one　of　the　key　factors　affecting　routability.　With　the　continuous　reduction　in　feature　sizes,　the　short-circuit　problem　grows　significantly　in　detailed　routing.　Track　assignment,　as　a　crucial　intermediary　phase　between　global　routing　and　detailed　routing,　plays　a　vital　role　in　preprocessing　the　short-circuit　problem.　However,　existing　track　assignment　algorithms　face　the　challenge　of　easily　falling　into　local　optimality.　As　a　typical　swarm　intelligence　technique,　particle　swarm　optimization　(PSO)　is　a　powerful　tool　with　excellent　optimization　ability　to　solve　large-scale　problems.　To　address　the　above　issue,　we　propose　an　effective　track　assignment　algorithm　based　on　social　learning　discrete　particle　swarm　optimization　(SLDPSO-TA).　First,　an　effective　wire　model　that　considers　the　local　nets　is　proposed.　By　considering　the　pin　distribution　of　local　nets,　this　model　extracts　and　allocates　more　segments　to　fully　leverage　the　role　of　track　assignment.　Second,　an　integer　encoding　strategy　is　employed　to　ensure　that　particles　within　the　encoding　space　range　correspond　one-to-one　with　the　assignment　scheme,　effectively　expanding　the　search　space.　Third,　a　social　learning　mode　based　on　the　example　pool　is　introduced　to　PSO,　which　is　composed　of　other　particles　that　are　superior　to　the　current　particle.　By　learning　from　various　objects　in　the　example　pool,　the　diversity　of　the　population　is　improved.　Fourth,　a　negotiation-based　refining　strategy　is　utilized　to　further　reduce　overlap.　This　strategy　intelligently　transfers　and　redistributes　wire　segments　in　congested　areas　to　reduce　congestion　across　the　entire　routing　panel.　Experimental　results　on　multiple　benchmarks　demonstrate　that　the　proposed　SLDPSO-TA　can　achieve　the　best　overlap　cost　optimization　among　all　the　existing　methods,　effectively　reducing　congestion　in　critical　routing　areas.