解复杂连续函数优化问题的动态量子遗传算法

摘要

研究了一种解复杂连续函数优化的动态量子遗传算法（DQGA）。设计一种动态量子旋转角的更新策略及量子门调整策略，以加快算法收敛速度，同时为淘汰适应度差的个体，量子旋转策略表中动态地嵌入了变异算子。在算法进化后期引入灾变算子使算法及时跳出局部最优，避免早熟收敛。五个复杂连续函数的测试实验表明：所提算法对复杂连续函数优化问题的寻优能力较 QGA 更强，算法的稳定性更高，算法的迭代次数亦优于传统量子遗传算法。%A complex continuous function optimization of dynamic quantum genetic algorithm (DQGA ) is studied .A dynamic update strategy of quantum rotation angle and quantum gate adjust strategy is designed to speed up the algorithm convergence speed ,at the same time for the elimination of poor fitness individuals ,the mutation operator dynamically is em-bedded in the quantum rotation strategy table .Introducing the cataclysm operator in the late evolution algorithm makes the algorithm timely and jump out of local optimum ,premature convergence is avoid .Five complex continuous functions of the test results show that the proposed algorithm’s optimization ability for optimization of complex continuous function is stron-ger than QGA ,the stability of the algorithm is higher ,the iteration number of the algorithm is superior to traditional quan-tum genetic algorithm .