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Particle Swarm Optimization (PSO) is a population based stochastic algorithm for continuous optimization inspired by social behavior of bird flocking or fish schooling that has been successfully applied in different areas. However, its potential in discrete problems has not been sufficiently explored. Recent works have proposed hybridization of PSO using local search and Path relinking algorithms with promising results. This paper aims to present a hybrid PSO algorithm that uses local search and Path relinking too, but differently to the previous approaches, this work maintains the main PSO concept for the update of the velocity of the particle. The paper describes the proposed algorithm and a set of experiments with the Traveling Salesman Problem (TSP). The hybrid algorithm shows competitive results compared to other state of the art metaheuristics.