Adaptive particle swarm optimization neural network genetic algorithm in nonlinear function optimization extreme

In order to more accurate for nonlinear function extreme, this paper used improved particle swarm optimization neural network combining with genetic algorithm method to solve the problem. In view of the particle swarm optimization algorithm is easy to appear “premature” faults, introducing the adaptive threshold, initializing particles if they were under the constraint conditions, making particles jump out to the optimal value of the position in previous search. Through the experiment, contrasts to the genetic neural network algorithm and traditional BP neural network, this method is faster in convergence and has the smallest prediction error. Finally, combining with genetic algorithm, calculating the extreme value of nonlinear function by using the above three kinds of neural network trained forecast as an individual output fitness value. The adaptive particle swarm optimization neural network proves the most close to the theoretical calculation. It shows that the method is effective.