Research And Application Of Quantum Particle Swarm Optimization Algorithm

Due to the limitations of traditional methods in solving complex problems,population-based intelligent optimization(PIO)arises as the times require by simulating the macro-swarm intelligence behavior of social organisms and learning the specific phenomena or hidden rules in nature.With the deepening of the interdisciplinary interaction,the combination of quantum computing and traditional swarm intelligence optimization makes various kinds of quantum swarm intelligence optimization Algorithms proposed.As one of the most classical methods,quantum particle swarm optimization(QPSO)improves the performance of the particle swarm optimization(PSO)algorithm to a certain extent by improving the evolutionary search strategy,however,there are still some shortcomings such as weak search ability of single particle,ordinary performance of global search,and prone to premature convergence.This thesis will focus on the research and application of QPSO.To overcome the shortcomings of QPSO,a hybrid QPSO algorithm is proposed,then the new algorithm is optimized and applied to solve the flexible job shop scheduling problem(FJSP).The specific research contents are as follows:1.In order to overcome the shortcomings of existing algorithms in solving highdimensional complex problems with low convergence precision and slow search efficiency,a hybrid quantum particle swarm optimization algorithm is proposed.Firstly,the individual is coded by probabilistic amplitude coding method to extend the particle swarm search range.Secondly,the updated formula of quantum particle swarm algorithm is integrated into the rotation angle formula of the quantum revolving gate to update the particle position.Finally,quantum non-gates are used for mutation.The simulation results show that the method can effectively optimize the results of the problem and improve the algorithm search efficiency.2.In order to solve the problem that the mutation amplitude of quantum nongates is large and the optimal solution is easily lost in the mutation process,a hybrid quantum particle swarm optimization(QPSO)algorithm based on Lévy flights is proposed.Using the abnormal transport and non-standard statistical behavior of Lévy flights,the non-gate was replaced by a revolving gate combined with Lévy flights.The experimental results show that the addition of Lévy flights can improve the randomness of the algorithm search,improve the premature convergence of the algorithm,and retain the better population.3.In order to extend the application scope of the algorithm,the new algorithm is applied to the flexible shop scheduling problem.Firstly,the algorithm is applied to the sub-problem of process scheduling.Secondly,a random selection method based on probability is added to the machine selection process.Finally,an elite preservation strategy based on neighborhood search is added to the iteration process.Experiments show that each improvement method has a positive effect on improving the algorithm to solve flexible job shop scheduling problems.