Abstract: An improved quantum genetic algorithm (IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e.local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.