Sequential Approximate Multiobjective Optimization Using Computational Intelligence

In many practical problems, in particular in engineering design, the function form of criteria is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained by real/computational experiments such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. Usually, these experiments are time consuming and expensive. One of recent trends in optimization is how to treat these expensive criteria. In order to make the number of these experiments as few as possible, optimization is performed in parallel with predicting the form of objective functions. This is called sequential approximate optimization with meta-modeling. It has been observed that techniques of computational intelligence can be effectively applied for this purpose. This talk will discuss several issues in sequential approximate multiobjective optimization using computational intelligence.