Artificial glowworm swarm optimization algorithm is a new research orientation in the field of swarm intelligence recently. The algorithm has achieved success in the complex function optimization, but it is easy to fall into local optimum, and has the low speed of convergence in the later period and so on. Simulated annealing algorithm has excellent global search ability. Combining their advantages, an improved glowworm swarm optimization algorithm was proposed based on simulated annealing strategy. The simulated annealing strategy was integrated into the process of glowworm swarm optimization algorithm. And the temper strategy was integrated into the local search process of hybrid algorithm to improve search precision. Overall performance of the Glowworm swarm optimization was improved. Simulation results show that the hybrid algorithm increases the accuracy of solution and the speed of convergence significantly, and is a feasible and effective method.