Hybrid Algorithm with Variants for Feed Forward Neural Network

Levenberg-Marquardt back-propagation algorithm, as a Feed forward Neural Network (FNN) training method, has some limitations associated with over fitting and local optimum problems. Also Levenberg-Marquardt back-propagation algorithm is opted only for small network. This research uses hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) in FNN training. This algorithm includes a number of components that gives advantage in the experimental study. Variants such as size of the swarm, acceleration coefficients, coefficient constriction factor and velocity of the swarm are proposed to improve convergence speed as well as to improve accuracy. The integration of components in different ways in hybrid algorithm produces effective optimization of back propagation algorithm. Also, this hybrid evolutionary algorithm based on PSO can be used for complex neural network structure.