Research And Application Of Quantum Optimization Algorithm

Quantum optimization algorithm is a new type of intelligent optimization algorithm which combines quantum computing and intelligent optimization.However,when optimizing complex functions and multi-peak functions,the convergence rate of quantum optimization algorithm often drops and it is easy to fall into local convergence.In this paper,quantum genetic algorithm and quantum particle swarm optimization algorithm are improved and applied to practical problems.The details are as follows:1.Aiming at the defects of quantum genetic algorithm in complex continuous function optimization,such as slow convergence speed and easy to be trapped into local optimum,a quantum genetic algorithm based on improved multi-habitat crowding algorithm is proposed.The basic idea is:while retaining the advantage of fast search speed of multi-habitat crowding algorithm,clustering analysis is introduced to improve its search ability,and then the improved multi-habitat crowding algorithm is introduced into the quantum genetic algorithm.Simulation results show that compared with the basic quantum genetic algorithm(QGA),the multi-habitat crowding out QGA has improved the global convergence and the speed of convergence.2.Aiming at the precocious defect of conventional quantum particle swarm optimization(GPSO),the niche strategy is introduced into QPSO.The basic idea is:the population is divided into several small populations by niche technology,the solution space is divided into different search domains,and the different local best points are searched synchronously to avoid premature convergence.The algorithm is applied to the open vehicle routing problem and an example is given to verify the effectiveness of the algorithm.3.Aiming at the defects of precocity of basic genetic algorithm and poor local search ability,an improved quantum genetic algorithm was applied to optimize the path of UAV life signs detection.The basic idea is:on the basis of the basic quantum genetic algorithm,according to the gradient of the objective function,the Angle of the quantum revolving door can be determined adaptively.Numerical experiments show that the improved algorithm has better local convergence and faster convergence rate than the basic quantum genetic algorithm,and can obtain better life signs detection path than the basic quantum genetic algorithm.