Research On Swarm Intelligence Algorithm In Wireless Sensor Network

The emergence of swarm intelligence optimization algorithms provides new tools for solving various problems.However,easy to fall into local optimum and low convergence accuracy are the disadvantages of them.Three swarm intelligence optimization algorithms are studied in this paper,which are grey wolf optimizer(GWO),butterfly optimization algorithm(BOA)and particle swarm optimization(PSO).Aiming at the deficiencies of the algorithms,a variety of improved strategies are proposed,which are chaotic map initialization,chaotic adjustment strategies of control parameters,nonlinear control strategies,hybrid algorithms,etc.The Wilcoxon rank sum test and Friedman rank test of various benchmark functions test results are used to verify the performance of the improved algorithm.The HPSBA is applied to the two-dimensional node deployment problem of wireless sensor network(WSN).For the three-dimensional coverage problem of WSN,an improved virtual force three-dimensional covering algorithm(3D-IVF)is proposed.In this paper,the specific research and innovation of the optimization algorithm improvement,WSN 2D node deployment and 3D coverage problem are as follows:(1)The chaotic bifurcation phenomena and the corresponding Lyapunov exponent values of 20 one-dimensional chaotic maps are analyzed.In addition,the convergence of the GWO algorithm is proved by combining Markov chain theory.(2)A novel chaos grey wolf optimization algorithm(CGWOA)is proposed,the20 chaotic maps are used to improve the population and control parameters in the CGWOA,respectively.Four benchmark functions are used to verify the performance of the proposed CGWOA.Then,a new hybrid Cubic map and butterfly optimization algorithm with particle swarm optimization(HPSOBOA)is proposed for solving the high-dimensional optimization problems.(3)A novel hybrid particle swarm-butterfly algorithm(HPSBA)is proposed based on research in HPSOBOA,it is used to solve the two-dimensional node deployment of WSN.(4)Aiming at the three-dimensional coverage of WSN,the virtual force parameters between end nodes in the three-dimensional space are analyzed,and a three-dimensional coverage algorithm that improves the virtual force is proposed.The experimental results show that for the optimization of the test functions,the hybrid algorithm HPSBA is better than HPSOBOA,the chaotic improved GWO algorithm and other comparative optimization algorithms.In the optimized deployment of WSN two-dimensional nodes,HPSBA has better deployment coverage and network connectivity compared to five algorithms such as PSO,BOA,and IGWO.Likewise,compared with the 3D-VF algorithm,the coverage effect of 3D-IVF algorithm node coverage simulation has been improved to a certain extent in three-dimensional space.