Abstract
In many engineering design problems, the explicit function form of objectives/constraints can not be given in terms of design variables. Given the value of design variables, under this circumstance, the value of those functions is obtained by some simulation analysis or experiments, which are often expensive in practice. In order to make the number of analyses as few as possible, techniques for model predictive optimization (also referred to as sequential approximate optimization or metamodeling) which make optimization in parallel with model prediction have been developed. In this paper, we discuss several methods using computational intelligence for this purpose along with applications to multi-objective optimization under static/dynamic environment.
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