This research explores the impact of structural limiting perception on the performance of Particle Swarm Optimization by restricting the range of information sharing among particles. By introducing localized communication models through Ring and Tree topologies, the study demonstrates significant improvements over the standard global-best PSO, particularly on a range of Traveling Salesman Problem instances from the TSPLIB. The results show that constraining particle perception enhances both solution quality and convergence behavior, with the Tree topology emerging as the most effective structure. The topological modifications maintain swarm diversity, prevent premature convergence, and facilitate continuous exploration while exploiting promising search regions. These findings suggest that structural constraints on information sharing can enhance PSO’s robustness and effectiveness without adding computational complexity, offering a flexible approach applicable to various PSO variants and problem domains beyond TSP.