A Random Time-varying Particle Swarm Optimization for Local Positioning Systems

The particle swarm optimizer (PSO) is a stochastic, populationbased optimization technique that can be applied to a wide range of applications. This paper presents a random time variable PSO algorithm, called the PSO-RTVIWAC, introducing random timevarying inertia weight and acceleration coefficients to significantly improve the performance of the original algorithms. The PSO-RTVIWAC method originates from the random inertia weight (PSO-RANDIW) and time-varying acceleration coefficients (PSO-TVAC) methods. Through the efficient control of search and convergence to the global optimum solution, the PSO-RTVIWAC method is capable of tracking and optimizing in the highly nonlinear dynamic local positioning systems. Experimental results are compared with three previous PSO approaches from the literatures, showing that the new optimizer significantly outperforms previous approaches. Simply employing a few particles and iterations, a reasonable good positioning accuracy is obtained with the PSO-RTVIWAC method. This property makes the PSO-RTVIWAC method become more attractive since the computation efficiency is improved considerably, i.e. the computation can be completed in an extremely short time, which is crucial for the real-time local positioning systems. Our experiments on the PSO-RTVIWAC to track and optimize the tag position have demonstrated that it is especially effective in dealing with optimization functions in the nonlinear dynamic environments.