一种基于云模型的改进型量子遗传算法*

针对量子遗传算法在函数优化中易陷入局部最优和早熟收敛等缺点，采用云模型对其进行改进，采用量子种群基因云对种群进化进行定性控制，采用基于云模型的量子旋转门自适应调整策略进行更新操作，使算法在定性知识的指导下能够自适应控制搜索空间范围，能在较大搜索空间条件下避开局部最优解。典型函数对比实验表明，该算法可以避免陷入局部最优解，能提高全局寻优能力，同时能以更快的速度收敛于全局最优解，优化质量和效率都要优于遗传算法和量子遗传算法。

Improved quantum genetic algorithm based on cloud model theory

Quantum genetic algorithm for optimization in function easily falls into local optimal solution and the premature quickly converges of such shortcomings. This paper improved the use of cloud models of quantum genetic algorithm, using quantum cloud-to-species evolution of gene populations and qualitative operation of the control and quantum based on cloud model adaptive strategy revolving door update operation, so that qualitative knowledge of the algorithm could be adaptive control under the guidance of the scope of the search space, and their best under the conditions of the larger search space to avoid the local optimal solution. A typical function of comparative experiment results show that the algorithm can avoid trapping in local optimal solution, and enhance the ability of global optimization at the same time be able to more quickly converge to the global optimal solution. The quality and efficiency of optimization is better than the genetic algorithm and quantum genetic algorithm.