Application on Target Localization Based on Adaptive Particle Swarm Optimization Algorithm

This paper proposes a method of target location based on the adaptive particle swarm optimization algorithm in view of the shortcoming that the current localization solution is complex, and the standard particle swarm optimizer algorithm has low convergence rate and is easy to be trapped in local optimum. In the algorithm, the adaptive inertia weight can balance global and local search ability, and an adaptive mutation is developed to improve its abilities of PSO to jump out of the local optima and conduct local search. The experimental results on target localization have demonstrated that the new algorithm can achieve much higher localization accuracy, and the superiority is obvious in terms of solution quality and convergence rates.