Sequential Approximate Multiobjective Robust Optimization using ComputationalIntelligence and its Applications to Engineering Problems

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In many practical engineering problems, objective functions can not be given explicitly in terms of design variables, but the solution is searched in parallel with refining the mathematical model adding experimental samples sequentially. Therefore, model errors appear inevitably. In addition, implementation errors are also inevitable in practical problems. On this circumstance, it is desirable to obtain a solution which is optimal and robust in the sense that is insensitive against those errors. In this research, robust multiobjective optimization is studied under sequential approximate modeling using computational intelligence.