Particle swarm optimization algorithm is a typical intelligent optimization algorithm,its idea originates from the bird swarm foraging behavior in nature,compared with other intelligent algorithms,the algorithm has the advantages of simple and easy implementation,fast convergence speed and less parameters to be adjusted.Therefore,since the proposal has been received a lot of attention by many scholars,and become one of the international intelligent computing research hotspots,and then arose a variety of improved algorithms,and widely applied in economic and financial,logistics management,network security,image processing,data mining and other fields.In this thesis,some research results of particle swarm optimization(PSO)are sorted out,and then some improved PSO's are proposed on the basis of previous studies.The optimization algorithm is applied to solve the traveling salesman problem,path planning problem,location problem.The thesis is arranged as follows:1.Two improved particle swarm optimization algorithms based on parameters are proposed,one is that the inertial weight is guided with the adaptive change of the evolutionary rate,the other is that the population size is dynamically adjusted with the evolutionary rate.Numerical simulation results show that the optimization performance of the two improved algorithms is better than that of the basic particle swarm optimization algorithm.2.Three kinds of hybrid particle swarm optimization algorithms are proposed:Firstly,the replication operation and migration operation in the bacterial algorithm are integrated into the particle swarm optimization model,and the particle swarm optimization algorithm is given.Secondly,the concentration selection mechanism in the immune algorithm is integrated into the particle swarm optimization algorithm,and the immune particle swarm optimization algorithm is proposed.Thirdly,the chaotic mutation strategy is integrated into the particle swarm optimization model and the chaotic particle swarm optimization algorithm is given.Numerical experiments show that the three hybrid particle swarm optimization algorithms have stronger global optimization ability.3.The bacterial particle swarm optimization algorithm is applied to solve the robot path planning problem,then application of immune particle swarm optimization algorithm for location selection of logistics centerthe,and the chaotic particle swarmoptimization algorithm is applied to solve the traveling salesman problem,and the simulation results are well calculated. |