  This paper is a commentary on the work of Zain et al. (2011a) in which they
proposed the use of soft computing techniques, i.e., genetic algorithm (GA) and
simulated annealing (SA), to estimate optimal process parameters of the
abrasive waterjet machining. We found a fundamental problem with their work;
the authors have misinterpreted the original work of Caydas and Hascalik (2008)
in which the real problem is how to build predictive models for the machining
process, not to optimize the model variables using the estimate model as
presented by Zain et al. (2011a). In addition to this problem, the objective
function to be optimized is an infinitely differentiable quadratic formula with
box constraints, thus instead of using soft computing techniques, gradient
methods that have better convergence property and are usually faster should be
employed first. In particular, we show that the active set method outperforms
GA and SA both in the machining performance and computational times (about 13
times faster than GA and 462 times faster than SA).
