A Solving Algorithm for Nonlinear Bilevel Programing Problems Based on Human Evolutionary Model

Abstract

An algorithm based on the human evolutionary model is proposed for solving nonlinear bilevel programing problems. In view of the hierarchical structure of this problem, the algorithm is designed through feeding back the optimal solution of the lower-level problem to the upper-level. Based on the quality of individuals at each iteration, this proposed algorithm can independently change the population size to achieve the balance between global and local searching ability during the progress of evolution, which can perform an exhaustive search in the whole landscape through creating an individual by using the tabu search method. Finally, we test four typical bilevel programing problems by using the proposed algorithm to verify its feasibility. The experimental results indicate the proposed algorithm can not only solve bilevel programing problems but also get the global optimal solution.