CAJ | 학술논문

现有基于 Bloch 球面坐标的量子进化算法存在收敛速度慢和鲁棒性不稳定的问题。为此，提出基于斐波那契特性更新的自适应量子遗传算法。在最优解的搜索过程中，考虑目标函数在搜索点的变化率，建立自适应因子λ，反映搜索点处目标适应度值相对于相邻两代最佳目标函数值一阶差分的变化，调整λ以改善算法收敛的方向和速度。分析量子旋转门转角步长调整策略，建立基于斐波那契数列特性的转角步长函数Δφ和Δθ的更新规则。应用该算法求解多维复杂函数的极值优化问题，时间复杂度理论分析和仿真结果证明，该算法在收敛速度、效率和稳定鲁棒性等方面均有明显改善。
현유기우 Bloch 구면좌표적양자진화산법존재수렴속도만화로봉성불은정적문제。위차，제출기우비파나계특성경신적자괄응양자유전산법。재최우해적수색과정중，고필목표함수재수색점적변화솔，건립자괄응인자λ，반영수색점처목표괄응도치상대우상린량대최가목표함수치일계차분적변화，조정λ이개선산법수렴적방향화속도。분석양자선전문전각보장조정책략，건립기우비파나계수렬특성적전각보장함수Δφ화Δθ적경신규칙。응용해산법구해다유복잡함수적겁치우화문제，시간복잡도이론분석화방진결과증명，해산법재수렴속도、효솔화은정로봉성등방면균유명현개선。
The current quantum evolution algorithms based on the Bloch spherical coordinates have slow convergence rate and poor robustness. Aiming at the two shortages, a new self-adaptive Quantum Genetic Algorithm(QGA) which is based on the characteristic of Fibonacci sequence is proposed. In the process of searching the optimal solution, a self-adaptive factorλis introduced to reflect the relative change rate which is relative to the difference of the best individual’s objective fitness between the parent generation and the child generation. The convergence rate and direction of the algorithm can be improved by adjusting the factor. It is constructed the rule of updating the rotation angleΔφandΔθwhich is based on Fibonacci sequence by studying its properties. Using the new algorithm to deal with the multidimensional complex functions, theoretical analysis of algorithm time complexity and the simulation results show that the new algorithm improves the convergence rate, efficiency and stability robustness.