Research On Improved Quantum Ant Colony Algorithm In Dynamic Optimal Route Guidance

With the rapid development of the economy,the car ownership has increased every year,urban traffic congestion and environment pollution is worsening.Intelligent Transportation Systems(ITS)has been recognized as the best way to effectively solve the problem of urban traffic.Traffic optimal route guidance in ITS can plan out a reasonable path dynamically according to the requirements of the traveler's planning,which can improve the efficiency of the road traffic system.The solution is to build an effective network model,and to use high efficient intelligent algorithm to achieve the optimal path guidance.But the existing road network model in considering traffic information is not perfect,the guidance algorithm always has the shortage of slow convergence speed and easily falling into the most superior.To solve these problem,firstly,on the basis of the static network model and the static shortest path model,according to the graph theory and dynamic traffic assignment theory,this paper constructs a dynamic road network model based on dynamic information of the road crossing and the road segment,which considers the queuing delays and turn delays of the intersection and road travel time.An improved TPI index as traffic congestion evaluation is considered.And then,methods for calculating the energy consumption of transport are defined.On that basis,a dynamic route guidance model is established,based on distance,time,energy consumption separately.Secondly,in view of the traditional ant colony algorithm's shortage of slow convergence speed and easy to fall into local optimum,by introducing the principle of quantum computing and quantum ant colony algorithm,an improved quantum ant colony algorithm is suggested.Quantum bit coding is taken to describe ant location information.By narrowing the scope of the qubit phase angle,and introducing an adjustment factor k in double chain code,the probability amplitude values range is ensured,and the coding space is compressed in order to improve the search space of the algorithm.To solve the problem that qubit does not change the amplitude in the process of quantum NOT gate variation,which may cause the local optimal problem,Hadamard gate is introduced to the mutation mechanism.Thus,phase angle of rotation can not only achieve the location of the qubit's two probability amplitudes swap,but also change the amplitude of the qubit,expand population diversity.By analyzingclassical function's optimization,improved quantum ant colony algorithm can expand the search space,increase the diversity of the population,improve the convergence speed,which has high search precision and not easily to fall into local optimal solution.Finally,IQACA is applied to the optimal route guidance of the dynamic road network.Road network model is abstracted and the fitness function of the algorithm is designed respectively,according to the distance,time and energy consumption.Simulation shows that improved quantum ant colony algorithm is fit for optimal route guidance,which has a good optimization results and can quickly search the global optimal solution.