Abstract
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.
Highlights
Evolutionary algorithms have been applied since their origins [1] in the mechanical and structural optimization field
In this paper we introduce a methodology that consists of the combination of preference-based multiobjective evolutionary algorithms introducing aspiration levels as a reference point; here, the g-dominance is used, with engineering knowledge variance reduction in the Monte-Carlo simulation process for improving computational performance of robust design in structural engineering
Ten independent executions were performed for each multiobjective evolutionary algorithm
Summary
Evolutionary algorithms have been applied since their origins [1] in the mechanical and structural optimization field. This paper proposes to combine that approach with the consideration of the engineer/decision maker’s preference information as desirable aspiration levels for objective functions (reference point(s)) when using evolutionary multiobjective computation These aspiration levels are materialized in the case of robust design as the values of the fitness function of the optimum design of the deterministic problem (without considering uncertainties) as well as zero variation of the performance function, as proposed by Greiner et al [33]. In this paper we introduce a methodology that consists of the combination of preference-based multiobjective evolutionary algorithms introducing aspiration levels as a reference point; here, the g-dominance is used, with engineering knowledge variance reduction in the Monte-Carlo simulation process for improving computational performance of robust design in structural engineering. Where Ai is the area of the bar section type i; ρi is the density of bar i; li is the length of bar i; k is the constant that regulates the equivalence between weight and restriction (suitable values around one); violj, for each violated restriction j, is the quotient between the violated restriction value (stress, displacement, or slenderness) and its reference limit
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
