There isn't a built in way to do this. In version 22.0, you can define multiple optimizations up front. But there isn't an automated way to apply the optimal value found in a previous optimization to the next.
In most cases, however, this approach will yield worse results than optimizing all parameters together. When you optimize with only a single parameter, you'll have to determine what the other parameter values should be. If you choose different values for the constant parameters, the optimal value for the varying parameter will change. If you choose poor values for the other parameters on the first round, you'll get the best of a bad situation. If you then use that value in subsequent optimizations, you're locking in a choice that was made given the bad situation.
I bring that up because I think it would be better to run with all the parameters you plan to optimize, and just let the optimizer run for more iterations. In version 22.0, all the results of your model runs are saved in a separate file. If you run the optimizer again, you no longer have to re-simulate the scenarios that have already been run; the optimizer just looks up the results from the file. This makes it easy to run for a long while, get the best scenario, and then, if you like, use that scenario to start the next optimization.
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Jordan Johnson
Principal Software Engineer
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