Heuristic search techniques stand as stalwart allies in the pursuit of optimal solutions for the Traveling Salesman Problem (TSP), a conundrum ubiquitous across diverse industries including semiconductor manufacturing, logistics, and transportation. In this paper, an enhanced particle swarm optimization (E-PSO) is being proposed for TSP. A distinguishing feature of E-PSO lies in its departure from conventional PSO methodologies. Here, each particle within the swarm is not solely guided by the global best solution but also by a superior peer. This nuanced guidance mechanism serves as a safeguard against premature convergence, allowing the swarm to explore the solution space more comprehensively. The evaluation of the E-PSO method is being conducted by comparing its performance to that of 10 conventional TSP instances. The crux of this assessment revolves around two pivotal aspects: the algorithm’s acumen in discerning optimal solutions and its computational efficiency. The computational results provide evidence that the E-PSO algorithm shows remarkable improvement for solving TSPs, outperforming the PSO method.