关键词: 量子计算, 双链量子遗传算法, 变步长, 优化计算

Abstract: A self-adaptive variable step double chains quantum genetic algorithm is proposed in this paper, which improves the slow convergence rate and the poor robustness of the double chains quantum genetic algorithm based on real-code and gradient information. In the algorithm, the mathematical model is constructed which reflects change rate of objective fitness function. Coefficient k of variable step is established to reflect the relative change rate of the fitness at the current searching place. The searching process of the optimal solution can be improved by adjusting the coefficient k of variable step which affects the relative change rates of the fitness. The updating strategies of quantum revolving gate [Δθ] are constructed. The specific procedure of the algorithm is designed for the complex continuous space optimization problems. The results of simulation show that the algorithm can improve the robustness effectively and improve the convergence rate.

Key words: quantum computation, double chains quantum genetic algorithm, variable step, optimization algorithm