i've come to realize that my multiobjective optimization problem is far from simple, with a lot of functions with noise-induced multimodality (FNIM) (numerical noise creating local minima). so i've been looking for previous work on how to deal with it. the fnim literature i've found seems to be very navel-gazing in that they look at how global minima bifurcate at the noise level is increased. but not as much on how to overcome the problem.
the term 'robust optimization' seems to be dominated by some guys from stanford, mit/singapore, and israel who have a particular framework that is not so useful to me right now. they assume the data going into the objective function are from a distribution that is an unknown member of a set of possible distros, and they want to protect against the worst-case from that set while strictly obeying the constraints. i think it's more of a mini-max problem, and what i need is to account for the uncertainty without needing to be so conservative. also, most, though not all, of the applications have a linear programming bend.
i'm not sure if stochastic optimization is what i need, but i'll check it out.
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