A Novel Technique for Solving Integer Linear Bilevel Programming Problems

A novel technique that addresses the solution of the general integer linear bilevel programming problem to global optimality is presented i.e. the general case of bilevel linear programming problems where each decision maker has objective functions conflicting with each other. We introduce linear programming problem of which resolution can permit to generate the whole feasible set of the upper level decisions. The approach is based on the relaxation of the feasible region by convex underestimation. Finally, we illustrate our approach with a numerical example.

The Backpropagation Artificial Neural Network Based on Elite Particle Swam Optimization Algorithm for Stochastic Linear Bilevel Programming Problem

For a class of stochastic linear bilevel programming problem, we firstly transform it into a deterministic linear bilevel covariance programming problem. Then, the deterministic bilevel covariance programming problem is solved by backpropagation artificial neural network based on elite particle swam optimization algorithm (BPANN-PSO). Finally, we perform the simulation experiments and the results show that the computational efficiency of the proposed algorithm has a potential upside compared with the classical algorithm.