Training RBF Neural Network by hybrid Adaptive Modified Particle Swarm Optimization Tabu Search algorithm for Function Approximation
This days interests in metaheuristic optimization algorithms have been grown and this algorithms have been used in many fields Because of this many algorithms have born or mixed up together in order to get into better results In this paper a new hybrid AMPSO TS Adaptive Modified Particle Swarm Optimization Tabu Search method is presented for training RBF (Radial Basis Function neural network that used to function approximation This AMPSO TS method uses the search ability of Tabu Search algorithm to optimize the Adaptive Modified Particle Swarm Optimization to result in more accurate This hybrid method is used for learning RBF neural network and uses it for function approximation Both PSO and TS method use the strategy of best solution of neighborhood in every iteration to get into best result but every one of them has issues By use of advantages of this methods we proposed the new method that uses TS to optimize the constants of PSO numerical result show that the proposed method has improved results contrast to the simple PSO and gradient descent method to learn the RBF neural Network