Research And Application Of Improved Particle Swarm Optimization Algorithm In Wireless Sensor Network Coverage Problem

With the rapid development of computer technology networks,automation control,artificial intelligence and other disciplines,the acquisition of effective information data has attracted more and more attention.Then,wireless sensor networks(WSNs)has developed.WSNs is an intelligent network system that maintains adaptability in the network environment,has certain cognitive ability and can accomplish the corresponding mission objectives.It has broad research space and bright application prospects.In today's society,WSNs is widely used in civil and military fields,and has achieved very good results,becoming an important research direction in the field of information technology.As an intelligent optimization algorithm,Particle Swarm Optimization algorithm(PSO)has fast solution speed and strong ability to search for optimal solutions.In the process of searching,it is affected by the optimal position of individual search and the optimal position of the group.In this paper,it is applied to the wireless sensor network coverage problem.After analyzing the particle swarm optimization algorithm,it is found that the particle swarm algorithm can not get rid of the local optimum and get into an infinite loop after multiple iterations.The process of the wireless sensor network coverage optimization problem is more complicated and the amount of calculation is large,so the particle swarm algorithm needs to be improved and optimized.Aiming at the problems of slow convergence and easy localization of particle swarm optimization,this paper proposes a Virtual-Forced Particle Swarm Optimization(VFPSO)algorithm,which is used to solve the path planning and obstacles of robots.The idea of the artificial potential field algorithm,aiming at the optimization process of particle iteration in particle swarm optimization algorithm,introduces the virtual force between particles to make the initial solution distribution of the optimization problem more uniform.It has stronger repulsive force in the early stage of iterative optimization.The attraction of the anchor node is enhanced,thereby accelerate the convergence speed of the algorithm and obtain an optimal solution with high precision.Aiming at the low precision of particle swarm optimization algorithm,a particle swarm optimization algorithm based on Beetle Antennae Search Strategy(BASPSO)is proposed,which combining the search strategy of Beetle Antennae Search Strategy and the self-learning process of particle swarm optimization.The particle optimization path was changed and the function was tested to improve the experimental results.The algorithms are applied to the wireless sensor network coverage problem.Through several experiments,it is found that the VFPSO algorithm can make the particle distribution more uniform,avoiding the particle aggregation and causing the algorithm to fall into the local optimal solution.It is more suitable for small-scale coverage problems and can get a better layout.The BASPSO algorithm is suitable for a wider range of coverage problems due to its lower algorithm complexity,and it can obtain a better network layout than the standard particle swarm optimization algorithm.