  This paper proposes a hybrid multiagent learning algorithm for solving the
dynamic simulation-based bilevel network design problem. The objective is to
determine the op-timal frequency of a multimodal transit network, which
minimizes total users' travel cost and operation cost of transit lines. The
problem is formulated as a bilevel programming problem with equilibrium
constraints describing non-cooperative Nash equilibrium in a dynamic
simulation-based transit assignment context. A hybrid algorithm combing the
cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is
proposed. Computational results are provided on the Sioux Falls network to
illustrate the perform-ance of the proposed algorithm.
