As with all combinatorial optimization problems, finding efficient experimental designs can be difficult. For this reason, the OPTEX procedure provides a variety of ways to customize the search.
Although default settings make the procedure simple to use "as is," you can usually improve the search by using knowledge of the specific design problem. For example, if the default algorithm (EXCHANGE) runs quickly but it is not clear whether it finds the best design, you can try a slower but more reliable search method or use more iterations than the default number of 10.
The choice of candidate points can profoundly affect both the speed with which the search converges at a local optimum and the likelihood that this local optimum is indeed the global optimum. Up to a point, the more candidate points there are, the better the resulting optimum design will be but the longer it will take to find. Any prior knowledge that can be brought to bear on the choice of candidates will almost certainly improve the search. For example, for first- or second-order models it is usually adequate to restrict the candidates to just the center and the edges of the experimental region, or perhaps even less; refer to Snee (1985), and see the introductory examples in the sections Handling Many Variables and Constructing a Mixture-Process Design.
The reliability of the search algorithms in finding the optimal design can be quite sensitive to the choice of initial design. The default method of initialization for each search procedure should achieve good results for a wide variety of situations (see the INITDESIGN= option ). However, in certain situations it is better to override the defaults. For example, if there are many local optima and you want to find the exact global optimum, it will probably be best to start each try with a completely random design (INITDESIGN=RANDOM). On the other hand, prior knowledge might provide a specific initial design, which can be placed in a SAS data set and specified with the INITDESIGN= option.