An Improved Particle Swarm Optimization Algorithm And Its Application

The particle swarm optimization algorithm is a global random search algorithm obtained by simulating migration and clustering behavior during the foraging of birds.This paper first introduces the basic principles and implementation steps of the general particle swarm optimization algorithm,and analyzes some shortcomings of the basic particle swarm optimization algorithm,such as: easy precocity,poor optimization of discrete problems,and inability to deal with constrained optimization problems.Aiming at these shortcomings of the basic particle swarm optimization algorithm,this paper proposes a new particle swarm optimization algorithm to solve the shortcomings of the particle swarm optimization algorithm.Aiming at the premature phenomenon of particle swarm optimization,an inertia weight of wave falling is proposed.In the early iteration,the inertia weight of particles is larger.With the increase of iterative algebra,the inertia weight is fluctuating,which enhances the particle bounce local optimum.In order to solve the constraint optimization problem,a virtual boundary is proposed to increase the search ability of the population;and a particle evolution theory that violates the constraint is proposed to make the particle swarm optimization algorithm more efficient in solving the constrained optimization problem.Finally,for these improvements,three typical test functions were used for experimental verification.The experimental results show that the improved particle swarm optimization algorithm has a significant improvement in the speed and accuracy of the constrained optimization problem.Finally,based on the urban public travel,this paper analyzes the needs of the people's public travel and the related business of public transport profit,and establishes a model based on bus-people's maximization of interests.Considering that the trend of passenger flow is greatly affected by weather,road resistance and other factors,and some constraints must be considered from the business needs of the bus system,such as the working hours of drivers,passengers' waiting time,etc.,and finally using improved particle swarm optimization algorithm.The model is solved and the results show that the improved particle swarm optimization algorithm is efficient and feasible.