Specifications

252
as in the unconstrained case. The path of the design values bounces off the constraint and finally it gets
stuck on the constraint even though the objective function still has a downwards inclination. The constraint
lies like a wall through the design space. We can see the convergence path along the constraint by plot the
constraint value, i.e., the SeatDist.Val. This looks like:
where it is obvious how the optimizer hits the constraint, bounces off, hits again, etc. and finally it
converges. At no point in time, the constraint value becomes negative, which was exactly what we
prescribed in its definition.
A final look at the result could be the picture of the model after this constained optimization, which shows a
visible difference compared to the unconstrained solution: The hip position is now higher, i.e., longer from
the crank and to achieve this it is further forward, see the picture below: