Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing estimated gradient information to the velocity of each particle. The second explicitly incorporates the Nelder-Mead algorithm, a known local search technique, within PSO. The suggested methods have been applied to the problem of estimating parameters of a gene network model. Results indicate the effectiveness of the proposed strategies.