Particle swarm optimization (PSO) is a heuristic stochastic evolutionary algorithm. However, standard PSO exists unbalanced exploitation and exploration, lower convergence speed. An improved technique is introduced into the standard PSO with adaptive computation of the inertia weights. After every iteration, a new competition with a random swarm is operated to jump out of the local optimum. Four benchmark functions are selected to test the validate of the constructed algorithm. The numerical experiments results show that the proposed algorithm is effective. The convergence speed and accuracy were better than the comparison algorithm.