改进的量子遗传算法及应用

Improved quantum genetic algorithm and its application

针对量子遗传算法在函数优化中迭代次数多、容易陷入局部最优解等缺点,提出新的量子遗传算法。该算法的核心是采用新的量子旋转门调整策略对种群进行更新操作,有效保证了种群的多样性,可以避免算法陷入局部最优解,提高了算法的全局寻优能力。同时能以更快的速度收敛于全局最优解。通过对典型复杂函数测试,计算结果表明,提出的算法优化质量和效率都要优于传统遗传算法和一般量子遗传算法。

New methods are joined into the quantum genetic algorithm to solve the defects of poor local search ability and more iterative times. The algorithm is the core of a new quantum rotation gate adjustment strategy, which updates the population, so it has better diversity than the classical genetic algorithm, rapid convergence and good global search capacity characterize the performance of the quantum genetic algorithm. According to the test of typical complex function, the results show that the algorithm to the quality of optimization and efficiency are superior to traditional genetic algorithm and general quantum genetic algorithm.