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 factor λ is 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 angle Δφ and Δθ which 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.
Bloch spherical coordinates,
Quantum Genetic Algorithm(QGA),