Abstract: To the problems of premature convergence frequently appeared in Particle Swarm Optimization(PSO)algorithm and its poor convergence accuracy,a particle swarm optimization algorithm combining local search and quadratic interpolation was proposed．Firstly,we randomly chose three positions from the N positions which are generated by standard Particle Swarm Optimization algorithm,and the new position was generated by using quadratic interpolation operator for each particle,and the previous best position of each particle and the global best position of swarm were updated.Then after some iteration steps,the Hooke-Jeeves local search technique optimized the global best position of the swarm found so far．Finally,simulation experiment on a set of 9benchmark functions was given,and the comparisons with other algorithms were provided．The numerical results show that the proposed algorithm has a fast convergence speed and good global search capability.

Key words: Particle swarm optimization,Quadratic interpolation,Local search,Global optimization