Research of quantum tabu search algorithm

When optimizing high-dimensional function, the quantum intelligence algorithms is easy to fall into local optimum. 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 solution phase, and a local optimization processing method of candidate solution is added. In order to verify the validation of the algorithm, simulations are made in extreme value of high dimensional function and multidimensional knapsack problem. Experimental results show that the proposed algorithm owns fast convergence speed and high solution precision.