In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). First, the proposed algorithm considers local nets to better guide detailed routing and an effective encoding method is designed to adapt the evolutionary algorithms better. Second, the social learning mode based on example pool mechanism is presented to improve the algorithm performance. Finally, a negotiation-based refining strategy is utilized to further reduce the overlap. Experimental results show that SLDPSO-TA can obtain the best overlap cost optimization among similar works, and reduce the congestion in key routing areas.