Improved Particle Swarm Algorithm And Its Application In Sensor Network Localization

Particle swarm optimization algorithm is a kind of intelligent search algorithm and derived from the behavior of birds feed on simulation. Many scholars have done a lot of research on it, because of its simple definition and solving complex optimization problems effectively, this algorithm has been widely used in many fields. However, the theoretical research is still not perfect, and existing the problem of premature convergence. In order to improve these shortcomings, researchers have made many improvements to the algorithm, and has a certain effect. Based on that, this paper also studies the improvement of PSO method in detail to improve the performance of the algorithm and applies this method successfully in sensor network node localization.In this paper, we study the particle trajectory and the characteristics of the algorithm convergence, and respectively analyzed the differents between the simplified PSO system and generalized PSO system. In addition, the article also analysis the unrestrained trajectory,and reflect the Convergence, periodic and discreteness of the particle more intuitively. Based on the analysis of the premature convergence of ingredients and solutions, After research and analysis, the paper get the conditions of global convergence and local convergence of the algorithm. This paper based on the inertia weight, learning factor, contraction factor, hybrid algorithm of the improved pso algorithm research, in the research background of sensor node localization, make full use of the advantage of the chaos mapping, and combined with pso, puts forward a new improved algorithm. The result of the Simulation show that the algorithm can really optimize the sensor network node localization, improve the the efficiency and precision of the positioning and is a really feasible solutions in the localization of sensor nodes.