Engineering Knowledge-Based Variance-Reduction Simulation and G-Dominance for Structural Frame Robust Optimization

This paper proposes the incorporation of engineering knowledge through both (a) advanced state-of-the-art preference handling decision-making tools integrated in multiobjective evolutionary algorithms and (b) engineering knowledge-based variance-reduction simulation as enhancing tools for the robust optimum design of structural frames taking uncertainties into consideration in the design variables. The simultaneous minimization of the constrained weight (adding structural weight and average distribution of constraint violations) on the one hand and the standard deviation of the distribution of constraint violation on the other are handled with multiobjective optimization-based evolutionary computation in two different multiobjective algorithms. The optimum design values of the deterministic structural problem in question are proposed as a reference point (the aspiration level) in reference-point-based evolutionary multiobjective algorithms (here g-dominance is used). Results including S-metric statistics in a structural frame test case with uncertain loads show considerable reductions in computational costs without harming the nondominated front quality, obtaining a design set that makes it possible to select minimum weight and maximum robustness optimum designs.