In this paper a new hybrid Particle Swarm Optimization (PSO) algorithm is introduced which makes use of gradient information to achieve faster convergence without getting trapped in local minima. Simulation results comparing the standard PSO algorithm to the new hybrid PSO algorithm are presented. The De Jong test suite of optimization problems is used to test the performance of all algorithms. Performance measures to compare the performance of different algorithms are discussed. The new hybrid PSO algorithm is shown to converge faster for a certain class of optimization problems.