A good answer to this question depends on what you want to use the labels for.
When I think about "optimization," I think about a solution space and a cost function; that is, there are many possible answers that could be returned and we can know what the cost is of any particular answer.
In this view, the answer is "yes"--pattern recognition is a case where each pattern is a possible answer, and the optimization method is trying to find the one where the cost is lowest (that is, where the answer matches what you want it to match).
But most interesting optimization problems are characterized by exponential solution spaces and clean cost functions, and so can be thought of more as 'search' problems, whereas most pattern recognition problems are characterized by simple solution spaces and complicated cost functions, and it might feel unnatural to put the two of them together.
(In general, I do think that optimization and intelligence are deeply linked enough that optimization power is a good measure of intelligence, and certainly a better measure of the practical use of intelligence than pattern recognition.)