Improved Quantum Evolutionary Algorithm And Its Application In Optimization Problems

The quantum evolutionary algorithm is an intelligent optimization algorithm combines quantum computing and evolutionary algorithms.Based on the traditional evolutionary algorithm related concepts of quantum computing,quantum bit coding is used in the coding method,and the quantum gate updating method is utilized.The evolutionary search has higher search efficiency and convergence speed than traditional evolutionary algorithms.Therefore,the research on quantum evolutionary algorithms has theoretical value and application prospects.In this paper,in response to the quantum evolutionary algorithm is not capable to solve the complex optimization questions which are easier to fall into the local optimization problems.The niche strategy and particle swarm optimization algorithm are used to propose an improved quantum evolution algorithm,and use the standard functions and hub airport parking place allocation verification method to prove the method's effectiveness.Firstly,the combination schemes of different rotation directions and angles are studied.Many experiments are carried out to solve the complex function extremum problems.The experimental results prove the advantages of dynamically adjusting the quantum gate rotation.Then,using the niche strategy to initialize the population,and divide the population into several sub-populations,which improves the diversity of the population.Aiming at the problem of poor local search ability of particle swarm,a new learning factor determination method is proposed.The improved particle swarm evolution equation is introduced into the quantum rotation gate,and the rotation direction and angles axe dynamically adjusted to guide the chromosome evolution and replace the tradition.This method improves the programming efficiency and the ability of the algorithm to jump out of the local extremum.Through multiple sets of standard functions,improve the effectiveness of the algorithm to solve the optimization problem is verified.Finally,use the NCPQEA solve the problem of hub flight allocation.Choose the flight data from Guangzhou Baiyun Airport.Take aim of maximize the idle time of the parking space,the shortest walking distance for passengers and the fullest use of large parking spaces.The resulting average flight allocation rate reached more than 90%,and achieved good results to verify the effectiveness and feasibility of the algorithm.