  We focus on interval algorithms for computing guaranteed enclosures of the
solutions of constrained global optimization problems where differential
constraints occur. To solve such a problem of global optimization with
nonlinear ordinary differential equations, a branch and bound algorithm can be
used based on guaranteed numerical integration methods. Nevertheless, this kind
of algorithms is expensive in term of computation. Defining new methods to
reduce the number of branches is still a challenge. Bisection based on the
smear value is known to be often the most efficient heuristic for branching
algorithms. This heuristic consists in bisecting in the coordinate direction
for which the values of the considered function change the most "rapidly".
  We propose to define a smear-like function using the sensitivity function
obtained from the differentiation of ordinary differential equation with
respect to parameters. The sensitivity has been already used in validated
simulation for local optimization but not as a bisection heuristic. We
implement this heuristic in a branch and bound algorithm to solve a problem of
global optimization with nonlinear ordinary differential equations. Experiments
show that the gain in term of number of branches could be up to 30%.
