Quantum genetic algorithm(QGA)is derived from the integration of quantum computation and genetic algorithm.It is characterized with advantages like strong global optimization ability,fast convergence speed and small population size.It has been proved in the academic circle that quantum genetic algorithm is one effective solution to complicated problems.However,quantum genetic algorithm is also faced with shortcomings like many iteration times,slow convergence speed and easy to fall into local optimal solution.To this end,traditional quantum genetic algorithm is improved in this paper,which is specifically:(1)Basic principles of quantum calculation,genetic algorithm and quantum genetic algorithm are studied deeply and their merits and demerits are analyzed and summarized.(2)In order to improve the convergence speed and accuracy of the algorithm,one kind of improved quantum genetic algorithm based on niche is proposed.This algorithm introduces the niche coevolution strategy into the population initialization of the quantum genetic algorithm to make improvement.Quantum rotation gate and quantum non-gate mutation operation are adjusted dynamically during the update.Classical functions(function Schaffer and function DeJong)are used to verify the algorithm.(3)Quantum non-gate mutation operation is added to the quantum genetic algorithm improved based on niche,which makes it possible that the algorithm will lose excellent information during the process so that the algorithm falls into the possibility of local extremum.Therefore,Hadamard gate mutation operation is used to replace the quantum non-gate mutation operation so as to increase the population diversity while preventing the algorithm from losing excellent information during update and improve the global search ability of the algorithm.In order to verify the validity and feasibility of the algorithm,optimization verification has been validated by the classical test function,and the improved algorithm has been applied to the actual medical data analysis. |