Research And Implementation Of Optimization Of Site Selection Based On Intelligent Optimization Algorithm

With the rapid development of Communication technology,people quickly enter from 2G to 4G,and it is also said that 5G is ready to go.However,sites suitable for base station layouts become increasingly scarce.At the same time,a reasonable base station location plan in the entire Communication network planning is a crucial part.In the past,based on experience,manual design of candidate solutions was inaccurate and inefficient.Later,some scholars proposed mathematical modeling of the base station site selection,and then used the solution model to base station site selection.However,the base station site selection often needs to consider the coverage of the target area,the relationship between cost and traffic,and also consider signal interference and other factors,so the establishment of the model is relatively Complex.At the same time,the current intelligent optimization algorithm is not very good.Solve such Complex problems.The mainstream algorithms in intelligent optimization algorithms include particle swarm optimization algorithm and genetic algorithm.Therefore,it is of great significance to improve the genetic algorithm and particle swarm optimization algorithm and apply it to the base station's optimization location problem.In this context,this paper first analyzes the mainstream intelligent optimization algorithms.By studying the classical particle swarm optimization algorithm,the paper proposes the use of membrane Computing and METROPOLIS sampling to improve the particle swarm algorithm,and designs the PMET-PSO algorithm,in which METROPOLIS sampling is a particle swarm.The algorithm adds randomness so that it has the ability to jump out of local optimum and find the global optimal solution.The advantage of membrane Computing lies in parallelism,which can reduce the Complexity of the algorithm when solving Complex problems.By studying the genetic algorithm,the genetic algorithm is improved by using membrane Computing and PSO operator.The PPSO-GA algorithm is designed.The PSO operator is designed to update the position and velocity of the PSO algorithm as an operator instead of the mutation operator in the classical genetic algorithm,thereby enhancing the local search ability of the genetic algorithm and accelerating the convergence of the algorithm.The membrane Computing enhances the parallelism of the algorithm and reduces the time Complexity of the algorithm.Then by analyzing the traditional base station site selection mathematical model,a mathematical model is proposed to optimize the site selection for the base station to provide a reasonable candidate for the optimization of site selection.By simplification of the traditional model,necessary cost,coverage and signal quality are retained.Constraints are modeled.As the 4G network in urban areas has basically achieved full coverage,this paper selects the application scenario where the 4G full coverage area has not been implemented in the urban mountain area.Finally,ACIS configuration technology is used to build a three-dimensional CAD system.In the system,three-dimensional display of the base station site selection is performed.A set of three-dimensional base station planning system is designed for the convenience of people.The major innovations in this paper mainly include:(1)Design and implementation of a PMET-PSO algorithm based on METROPOLIS Sampling and Membrane Computing,and applying it to the base station site selection optimization mathematical model.(2)The genetic algorithm based on PSO operator and membrane computing(PPSO-GA)is designed and implemented,and it is used to solve the problem of base station location.(3)The use of ACIS technology,the use of C + + programming to develop a base station optimized site 3D CAD system,so that people can apply algorithms in the three-dimensional hierarchy,show solutions.