基于斐波纳契数列的自适应DCQGA

Self-Adaptive Double-chain Quantum Genetic Algorithm Based on Fibonacci Sequence

针对现有双链量子遗传算法的收敛速度慢、稳定鲁棒性差和时间复杂的特点,提出采用斐波纳契数列的自适应双链量子遗传算法。首先,研究了斐波那契数列的特性,建立了斐波那契数列的量子旋转门转角的调整策略;其次,在最优解的搜索过程中,考虑目标函数在搜索点的变化率,建立了随相邻两代的目标函数适应度值变化大小自适应地调节转角步长的方法;应用新算法求解复杂函数的极值优化问题。仿真结果表明,改进算法不仅提高了算法的收敛速度和稳定鲁棒性,而且明显的改善在算法的效率和降低算法的时间复杂度。

A new self-adaptive double-chain quantum genetic algorithm was proposed.Firstly,the rule of updating the rotation angle was constructed based on Fibonacci sequence by studying its properties.Secondly,in the process of searching the optimal solution,the step of rotation angleθ can be adjusted according to the change of objective function values between the parent generation and the child generation.Finally,the new algorithm was used to solve the complex functions with extreme value optimization problem.The simulation results show that the new algorithm can not only improve the convergence rate and stability robustness,but also boost strikingly the efficiency and reduce the time complexity.