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.