Improved Swarm Intelligence Optimization Algorithm And Its Application In WSN

As one of the meta-heuristic algorithms,the swarm intelligence optimization algorithm is inspired by the self-optimization phenomenon of biological group behavior in nature.The types of algorithms include: sparrow search algorithm,Harris eagle algorithm,whale optimization algorithm,particle Swarm algorithm,bacterial colony algorithm,etc.The swarm intelligence optimization algorithm has a simple structure,is relatively stable,and can support parallel computing.In this thesis,based on the sparrow search algorithm and the Harris eagle algorithm,improved algorithms are proposed for the two algorithms respectively.The optimization performance of the improved algorithm is verified by comparing the numerical experiments and simulation results,and the improved algorithm is applied to the WSN coverage optimization.The main research contents are as follows:(1)In view of the shortcomings of the original sparrow search algorithm,such as slow convergence speed,low convergence accuracy,and easy to fall into local optimum,this thesis proposes an improved sparrow search algorithm COSSA,which uses the chaotic reverse learning strategy and the adaptive spiral search strategy.Then,the improved algorithm is compared under the benchmark test function and chaotic map selection test,and by comparing the performance with other advanced algorithms,the radar chart of the test function in different dimensions,the mean convergence curve of different dimensions,and different test function boxes are simulated respectively.Finally,the Friedman test is used for multiple comparisons to verify the optimal performance of the COSSA algorithm.(2)Like other intelligent algorithms,the Harris Eagle algorithm also has certain problems in the optimization and solution of complex problems.The main reason is that when the population diversity decreases with the later iteration,the precocious phenomenon is easy to appear because it falls into the local optimal value,which makes it difficult to achieve the ideal.convergence accuracy.In order to improve the global search ability and prevent the reduction of population diversity in the later iteration,the improved Harris Eagle algorithm M-HHO uses the population initialization based on cubic mapping,distribution estimation algorithm,and Gaussian random walk strategy.In the comparative analysis of the M-HHO improvement strategy,it is compared with other algorithms,and the function box diagram and the convergence curve are simulated respectively,and the Wilcoxon signed rank table and the time cost table of the algorithm are drawn to verify the optimization performance of the M-HHO algorithm.(3)The improved algorithm is used to optimize the WSN coverage.The COSSA and MHHO algorithms are respectively applied to the WSN coverage optimization and compared with other intelligent optimization algorithms.The coverage curves obtained by simulation experiments in different area sizes Variation graphs are used to verify the performance of the two improved algorithms.