  The article proposes an exact approach to find the global solution of a
nonconvex semivectorial bilevel optimization problem, where the objective
functions at each level are pseudoconvex, and the constraints are quasiconvex.
Due to its non-convexity, this problem is challenging, but it attracts more and
more interest because of its practical applications. The algorithm is developed
based on monotonic optimization combined with a recent neurodynamic approach,
where the solution set of the lower-level problem is inner approximated by
copolyblocks in outcome space. From that, the upper-level problem is solved
using the branch-and-bound method. Finding the bounds is converted to
pseudoconvex programming problems, which are solved using the neurodynamic
method. The algorithm's convergence is proved, and computational experiments
are implemented to demonstrate the accuracy of the proposed approach.
