Q: During optimization, Evolver can not find any valid solutions. Why not?
A: Probably because your hard constraints are too tight. Each time Evolver generates a new trial solution, it calculates your constraints and determines if any of them were not met. If so, Evolver tosses out the solution. If it is impossible (or nearly impossible) for Evolver to meet your constraints, the optimization will never make any progress.
Keep the following tips in mind when designing your model
Range constraints (which are specified for the recipe and budget solving methods in the Adjustable Cells dialog) are by far the most efficient type of constraint. Evolver will never generate a trial solution that does not meet these constraints.
In many cases, soft constraints will work far better than hard constraints. For example, consider a model with the constraint
$A$1 = $A$2 * $A$3,
where the cells $A$1, $A$2, and $A$3 are all continuous adjustable input cells using the Recipe method. Entered as a hard constraint, Evolver will flounder, because most likely all the trial solutions will not meet this stringent constraint. Evolver will mercilessly toss every solution, without gaining any new knowledge of how to solve the problem. By making this a soft constraint, however, Evolver is able to generate solutions that don't exactly meet the constraint. As the optimization progresses, however, the solution will incrementally evolve closer and closer to a solution that does meet the constraint, while still optimizing your target cell.
