分析量子免疫克隆算法的基本原理,在此基础上,设计一种具有自适应学习的改进策略.该算法采用量子观测熵来度量算法的进化程度,并根据熵的变化自适应调整相应参数.从理论上证明该算法的收敛性,并且通过实验,比较量子免疫克隆算法、简单免疫克隆算法、量子进化算法的函数优化效果.仿真实验表明该算法能提高计算效率和搜索能力. On the basis of this, an improved strategy with adaptive learning is designed, which uses quantum observation entropy to measure the evolution of the algorithm and adjusts the corresponding parameters adaptively according to the change of entropy. The convergence of the proposed algorithm is proved theoretically, and the effects of quantum immune clonal algorithm, simple immune clonal algorithm and quantum evolutionary algorithm are compared through experiments, and the simulation results show that this algorithm can improve computational efficiency and search ability.