To enhance the optimization performance of the gravitational search algorithm, by introducing quantum computing to the original algorithm, a quantum-behaved gravitational search algorithm is proposed. The optimization strategies of the proposed algorithm are similar to the quantum-behaved particle swarm. In each of iteration, the fitness of each individual is evaluated, and then the quality of each individual is calculated based on its fitness. The first K greatest individuals are taken as a candidate set, in which an individual is randomly selected with roulette and is taken as the center of the Delta potential well. By adjusting other individuals move to the potential well centre, a single-step optimization is completed. In the process of optimization, the value of K is made decreasing monotonically to achieve a balance of exploration and exploitation. The experimental results on benchmark functions extreme optimization show that the proposed
algorithm is obviously superior to the original one in performance and efficiency.