A HAMPSO-RBF Algorithm Applied to Target Localization

This paper proposed a radial basis function neural network optimization algorithm with a hybrid adaptive mutation particle swarm. During the optimization of RBF neural networks, the HAMPSO method is adopted to train the network structure and applied to solve the problems of the target localization. The HAMPSO algorithm is a dynamically adaptive optimization approach using uniform distribution mutation and Gaussian distribution mutation to escape local optima. We propose a HAMPSO method that can expedite convergence toward the global optimum during the iterations. In order to verify that the proposed HAMPSO-RBF approach has effect, comparisons with the RBF, genetic algorithm based RBF and PSO-based RBF approach are made. The computational results proved that the proposed HAMPSO-RBF approach exhibits much better and faster convergence performance in the training process as well as better prediction ability in the validation process than the results of other three approaches.