量子禁忌搜索算法的研究

摘要

When optimizing high-dimensional function ,the quantum intelligence algorithms is easy to fall into local opti-mum .Aiming at the problems ,this paper proposes a quantum tabu search algorithm .A strategy which can drop as the index level and adjust dynamic circularly is proposed in the aspect of qubit phase incremental space ;a partitioning method which is relevant to the optimum solution in the tabu list and can adjust dynamically is proposed in the aspect of neighborhood space of the candidate so-lution phase ,and a local optimization processing method of candidate solution is added .In order to verify the validation of the algo-rithm ,simulations are made in extreme value of high dimensional function and multidimensional knapsack problem .Experimental re-sults show that the proposed algorithm owns fast convergence speed and high solution precision .%针对量子智能算法对高维函数的优化时存在容易陷入局部最优的问题，提出了量子禁忌搜索算法。在量子比特相位增量空间方面，提出了一种按指数级别下降并可动态循环调整的策略；在候选解相位邻域空间方面，提出了一种与禁忌表中最优解有关的可动态调整的划分方法，并增加了候选解局部优化处理方法。为了验证算法的有效性，在高维函数极值问题和多维背包问题进行了仿真，结果表明本文算法收敛速度快，求解精度高。