根据不同交叉算子的互补特性,提出了改进量子交叉免疫克隆算法(improved quantum crossover immune cloanl algorithm,IQCICA)。交叉算子由具有深度挖掘和广度挖掘特征的两种算子组成,并通过适当的参数控制两种算子的选择。将该算法应用于著名的组合优化问题—旅行商问题(traveling salesman problems,TSP),并将计算结果与其它算法进行了对比分析。仿真结果表明,混合量子交叉免疫克隆选择算法能有效平衡全局和局部搜索能力,有着较好的收敛速度和稳定性。 According to the complementary characteristics of different crossover operators, an improved quantum crossover immune cloanl algorithm (IQCICA) is proposed. The crossover operator consists of two operators with depth mining and breadth-of-mine features, and controls the choice of two operators by appropriate parameters. The algorithm is applied to famous combinatorial optimization problem - traveling salesman problems (TSP), and the results are compared with other algorithms. The simulation results show that the hybrid quantum cross-immune clonal selection algorithm can effectively balance the global and local search capabilities, and has good convergence speed and stability.