Article ID: | iaor20013097 |
Country: | United States |
Volume: | 12 |
Issue: | 4 |
Start Page Number: | 272 |
End Page Number: | 283 |
Publication Date: | Sep 2000 |
Journal: | INFORMS Journal On Computing |
Authors: | Wilson James R., Humphrey David G. |
Keywords: | optimization, programming: probabilistic |
We develop a variant of the Nelder–Mead (NM) simplex search procedure for stochastic simulation optimization that is designed to avoid many of the weaknesses encumbering similar direct-search methods – in particular, excessive sensitivity to starting values, premature termination at a local optimum, lack of robustness against noisy responses, and computational inefficiency. The Revised Simplex Search (RSS) procedure consists of a three-phase application of the NM method in which: (a) the ending values for one phase become the starting values for the next phase; (b) the step size for the initial simplex (respectively, the shrink coefficient) decreases geometrically (respectively, increases linearly) over successive phases; and (c) the final estimated optimum is the best of the ending values for the three phases. To compare RSS versus NM and procedure RS