Neural network approach for semivectorial bilevel programming problem

A novel neural network approach is proposed for solving semivectorial bilevel programming problem, where the upper level is a scalar-valued optimization problem and the lower level is the linear multiobjective programming. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the semivectorial BP problem. The numerical result shows that the neural network approach is feasible and efficient.